SALSA Lab: Home

SIGNAL ANALYSIS FOR LARGE SCALE APPLICATIONS

Next Generation Deep Learning

2022	Shalini Sharma and Angshul Majumdar A Deep State Space Model for Predicting Cryptocurrency Price , Information Sciences.
	Anurag Goel, Angshul Majumdar, Emilie Chouzenoux and Giovanni Chierchia, Deep Convolutional K-Means Clustering , ICIP 2022.
	R Krishna Kanth, A. Gigie, K. Kumar, A. A. Kumar, A. Majumdar and P. Balamuralidhar, Multi-Modal Image Super-Resolution with Joint Coupled Deep Transform Learning , EUSIPCO 2022.
	K. Aditi, A. A. Kumar, A. Majumdar, T. Chakravarty and K. Kumar, Phaseless Passive Synthetic Aperture Imaging with Regularized Wirtinger Flow , EUSIPCO 2022.
	J. Maggu, S. Sharma and A. Majumdar, Transductive Inversion via Deep Transform Learning , EUSIPCO 2022.
	Ronita Bardhan, Pooja Gupta and Angshul Majumdar, GeoInFuse - A data-driven information fusion for intra-urban form classification in data-scarce heterogeneous cities , CITIES: The International Journal of Urban Policy and Planning.
	Anurag Goel and Angshul Majumdar, K-means Embedded Deep Transform Learning for Hyperspectral Band Selection , IEEE Geoscience and Remote Sensing Letters.
	Divyanshu Talwar, Aanchal Mongia, Emilie Chouzenoux and Angshul Majumdar, Binary matrix completion on graphs: Application to collaborative filtering Digital Signal Processing, Vol. 122, 2022 (I.F. 3.381).
2021	Anurag Goel and Angshul Majumdar, Clustering Friendly Dictionary Learning , ICONIP 2021.
	Anurag Goel and Angshul Majumdar, Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification , IEEE Geoscience and Remote Sensing Letters.
	Aanchal Mongia and Angshul Majumdar, Matrix Completion on Learnt Graphs: Application to Collaborative Filtering , Expert Systems with Applications.
	Snehil Dahiya, Shalini Sharma, Dhruv Sahnan, Vasu Goel, Emilie Chouzenoux, Víctor Elvira, Angshul Majumdar, Anil Bandhakavi and Tanmoy Chakraborty Would your tweet invoke hate on the fly? Forecasting hate intensity of reply threads on Twitter , KDD 2021.
	Anurag Goel and Angshul Majumdar, Transformed K-means Clustering , EUSIPCO 2021.
	Arup Kumar Das, Kriti Kumar, Angshul Majumdar, Saurabh Sahu, M. Girish Chandra, Multi-Sensor Fusion Framework Using Discriminative Autoencoders , EUSIPCO 2021.
	Kriti Kumar, Saurabh Sahu, Angshul Majumdar and M. Girish Chandra, AUTOFUSE: A Semi-supervised Autoencoder based Multi-Sensor Fusion Framework , IJCNN 2021 (Accepted).
	S. Sharma, V. Elvira, E. Chouzenoux and A. Majumdar, Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting , Neurocomputing.
	Angshul Majumdar, Kernelized Linear Autoencoder , Neural Processing Letters.
	Andrew Gigie, Achanna Anil Kumar, Angshul Majumdar, Kriti Kumar and M Girish Chandra, Joint Coupled Transform Learning Framework for Multimodal image super-resolution , IEEE ICASSP 2021.
	Shalini Sharma and Angshul Majumdar, Sequential Transform Learning , Transactions on Knowledge Discovery from Data.
2020	SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems , Expert Systems With Applications.
	J. Maggu, E. Chouzenoux, G. Chierchia and A. Majumdar, Deep Convolutional Transform Learning , ICONIP 2020.
	A. Mongia and A. Majumdar, Deep Matrix Factorization on Graphs: Application to Collaborative Filtering , ICONIP 2020 .
	P. Gupta, J. Maggu, A. Majumdar, E. Chouzenoux and G. Chierchia, DeConFuse: A Deep Convolutional Transform based Unsupervised Fusion Framework , EURASIP Journal on Advances in Signal Processing (accepted) (1.7)
	J. Maggu, A. Majumdar, E. Chouzenoux and G. Chierchia, Deeply Transformed Subspace Clustering , Signal Processing, Vol. 174, 107628, 2020. (I.F. 4.0)
2019	A. Majumdar and M. Gupta, Recurrent Transform Learning , Neural Networks, vol. 118, pp.271-279, 2019 (I.F. 5.7).
	A. Gogna and A. Majumdar, Discriminative Autoencoder for Feature Extraction: Application to Character Recognition, Neural Processing Letters, Vol. 49 (3), pp 1723–1735, 2019 (I.F. 1.7).
	V. Singhal, A. Majumdar, M. Vatsa and R. Singh, Siamese Deep Dictionary Learning , IEEE IJCNN 2019.
	V. Singhal and A. Majumdar, Age and Gender Estimation via Deep Dictionary Learning Regression , IEEE IJCNN 2019.
	A. Majumdar, Deeply Coupled Graph Structured Autoencoder for Domain Adaptation , ACM CODS-COMAD 2019.
2018	A. Majumdar, Graph Structured Autoencoder Neural Networks, Vol. 106, pp. 271-280, 2018 (I.F. 7.1).
	J. Maggu, E. Chouzenoux, G. Chierchia and A. Majumdar, Convolutional Transform Learning, ICONIP, pp. 162-174, 2018.
	A. Paul, A. Majumdar and D. Mukherjee, Discriminative Autoencoder, IEEE ICIP, pp. 3049 – 3053, 2018.
	K. Seemakurthy, J. Gubbi, S. Deshpande, B. Purushothaman and A. Majumdar, Multi-spectral missing label prediction via restoration using deep residual dictionary learning, IEEE IJCNN 2018.
	V. Singhal and A. Majumdar, Supervised Deep Dictionary Learning for Single Label and Multi-Label Classification, IEEE IJCNN, pp. 1-7, 2018.
	T. Bose, A. Majumdar and T. Bhattacharya, Machine Load Estimation Via Stacked Autoencoder Regression, IEEE ICASSP, 2018.
	J. Maggu and A. Majumdar, Unsupervised Deep Transform Learning, IEEE ICASSP, pp. 6782-6786, 2018.
2017	V. Singhal, A. Majumdar and R. K. Ward, Semi-supervised Deep Blind Compressed Sensing for Analysis and Reconstruction of Biomedical Signals from Compressive Measurements, IEEE ACCESS, Vol. 6 (1), pp. 545-553. (I.F. 3.2).
	K. Gupta and A. Majumdar, Imposing Class-wise Feature Similarity in Stacked Autoencoders by Nuclear Norm Regularization Neural Processing Letters, (I.F. 1.7).
	V. Singal and A. Majumdar, Majorization Minimization Technique for Optimally Solving Deep Dictionary Learning, Neural Processing Letters, pp. 1- 16, 2017, (I.F. 1.7).
	I. Manjani, S. Tariyal, M. Vatsa, R. Singh, A. Majumdar, Detecting Silicone Mask based Presentation Attack via Deep Dictionary Learning, IEEE Transactions on Information Forensics and Security, Vol. 12 (7), pp. 1713-1723, 2017 (I.F. 4.3).
	A. Sankaran, M. Vatsa, R. Singh and A. Majumdar, Group Sparse Autoencoder, Image and Vision Computing, Vol. 60, pp. 64-74, 2017 (I.F. 1.7).
	A. Majumdar, A. Gogna and R. K. Ward, Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals, IEEE Transactions on Biomedical Engineering, Vol. 64 (9), pp. 2196 – 2205, 2017 (I. F. 2.5).
	A. Majumdar, M. Vatsa and R. Singh, Face Recognition via Class Sparsity based Supervised Encoding IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39 (6), pp. 1273-1280, 1 2017. (I.F. 8.3).
	A. Sankaran, G. Goswami, R. Singh, M. Vatsa and A. Majumdar, Class Sparsity Signature based Restricted Boltzmann Machines Pattern Recognition, Vol. 61, pp. 674-685, 2017. (I.F. 3.3).
	S. Nagpal, M. Singh, R. Singh, M. Vatsa, A. Noore and A. Majumdar, Face Sketch Matching via Coupled Deep Transform Learning IEEE ICCV, 2017.
	M. Singh, S. Nagpal, M. Vatsa, R. Singh, A. Noore, and A. Majumdar, Gender and Ethnicity Classification of Iris Images using Deep Class Encoder IEEE IJCB, 2017.
	J. Maggu and A. Majumdar, Greedy Deep Transform Learning IEEE ICIP, pp. 1822-1826, 2017.
	K. Gupta and A. Majumdar, Learning Autoencoders with Low-Rank Weights IEEE ICIP, 2017.
	A. Majumdar and R. K. Ward, Robust Greedy Deep Dictionary Learning for ECG Arrhythmia Classification IEEE IJCNN, 2017.
	A. Tripathi and A. Majumdar, Asymmetric Stacked Autoencoder IEEE IJCNN, 2017.
	V. Singhal, P. Khurana and A. Majumdar, Class-wise Deep Dictionary Learning IEEE IJCNN, pp. 1125-1132, 2017.
	V. Singhal and A. Majumdar, Noisy Deep Dictionary Learning: Application to Alzheimer's Disease Classification IEEE IJCNN, pp. 2679 - 2683 2017.
	A. Majumdar and R. K. Ward, Robust Greedy Deep Dictionary Learning for ECG Arrhythmia Classification CODS 2017.
	V. Singhal and A. Majumdar, Noisy Deep Dictionary Learning CODS 2017.
	V. Singhal, S. Singh and A. Majumdar, How to Train Your Deep Neural Network with Dictionary Learning Data Compression Conference, 2017.
	S. Singh, V. Singhal and A. Majumdar, Deep Blind Compressed Sensing Data Compression Conference, 2017.
2016	N. Kohli, M. Vatsa, R. Singh, A. Noore and A. Majumdar, Hierarchical Representation Learning for Kinship Verification, IEEE Transactions on Image Processing, Vol. 26 (1), pp. 289-302, 217. (I.F. 3.7).
	A. Gogna and A. Majumdar, Semi Supervised Autoencoder ICONIP, pp. 82-89, 2016.
	V. Singhal, A. Gogna and A. Majumdar, Deep Dictionary Learning vs Deep Belief Network vs Stacked Autoencoder: An Empirical Analysis ICONIP, pp. 337-344 2016.
	J. Mehta, K. Gupta, A. Gogna and A. Majumdar, Stacked Robust Autoencoder for Classification ICONIP, pp. 600-607, 2016.
	M. Gupta and A. Majumdar,, Nuclear Norm Regularized Robust Dictionary Learning for Energy Disaggregation EUSIPCO, 2016.
	K. Gupta and A. Majumdar, Sparsely Connected Autoencoder IEEE IJCNN 2016.
	P. Khurana, A. Majumdar and R. K. Ward, Class-wise Deep Dictionaries for EEG Classification IEEE IJCNN 2016.

Energy Analytics

2022	Angshul Majumdar, Disaggregating a New Appliance On-the-fly without Data Acquisition and Re-training , IEEE Transactions on Instrumentation and Measurement.
	Angshul Majumdar, Trainingless Energy Disaggregation without Plug-level Sensing IEEE Transactions on Instrumentation & Measurement.
2021	Shikha Singh, Angshul Majumdar, Emilie Chouzenoux and Giovanni Chierchia, Multi-label Deep Convolutional Transform Learning for Non-intrusive Load Monitoring , ACM Transactions on Knowledge Discovery from Data.
	Shikha Singh and Angshul Majumdar, Multi-label Deep Blind Compressed Sensing for Low-frequency Non-intrusive Load Monitoring , IEEE Power Engineering Letters.
	S. Sharma, A. Majumdar, Unsupervised Detection of Non-Technical Losses via Recursive Transform Learning , IEEE Transactions on Power Delivery.
2020	S. Singh, A. Majumdar and S. Makonin, Compressive Non-Intrusive Load Monitoring , BuildSys'20, short paper.
	S. Sharma, A. Majumdar, V. Elvira and E. Chouzenoux, Blind Kalman Filtering for Short-term Load Forecasting , IEEE Transactions on Power Systems.
	S. Singh and A. Majumdar, Non-intrusive load Monitoring via Multi-label Sparse Representation based Classification , IEEE Transactions on Smart Grids, vol. 11, no. 2, pp. 1799-1801, 2020 (I.F. 10.5)
	S. Paresh, N. Thokala, A. Majumdar and M. G. Chandra, Multi-Label Auto-Encoder based Electrical Load Disaggregation , IEEE IJCNN 2020.
	S. Singh, J. Maggu, A. Majumdar, E. Chouzenoux and G. Chierchia, Multi-Label Consistent Convolutional Transform Learning: Application to Non-Intrusive Load Monitoring , IEEE ICASSP 2020.
2019	M. Gaur, S. Makonin, I. V. Bajić and A. Majumdar, Performance Evaluation of Techniques for Identifying Abnormal Energy Consumption in Buildings , IEEE Access, vol. 7, pp. 62721-62733, 2019 (I.F. 3.5).
	V. Singhal, J. Maggu and A. Majumdar, Simultaneous Detection of Multiple Appliances from Smart-meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning, IEEE Transactions on Smart Grid, Vol. 10 (3), pp. 2969-2978, 2019 (I.F. 6.6).
	S. Singh and A. Majumdar, Analysis Co-Sparse Coding for Energy Disaggregation, IEEE Transactions on Smart Grid, Vol. 10 (1), pp. 462-470, 2019. (I.F. 6.6).
	S. Singh, S. Verma and A. Majumdar, Multi Label Restricted Boltzmann Machine for Non-Intrusive Load Monitoring , IEEE ICASSP, pp. 8345-8349, 2019.
2018	M. Gupta and A. Majumdar, Disaggregating Transform Learning for Non-Intrusive Load Monitoring IEEE ACCESS, vol. 6, pp. 46256 – 46265, 2018 (I.F. 3.2).
	M. Gulati, S. S. Ram, A. Majumdar and A. Singh, Single Point Conducted EMI Sensor With Intelligent Inference for Detecting IT Appliances, IEEE Transactions on Smart Grid, Vol. 9 (4), pp. 3716-3726, 2018 (I.F. 6.6).
	M. Gupta and A. Majumdar, Robust Supervised Sparse Coding for Non-Intrusive Load Monitoring, IEEE IJCNN 2018.
2017	S. Singh and A. Majumdar, Deep Sparse Coding for Non-Intrusive Load Monitoring IEEE Transactions on Smart Grid, Vol. 9 (5), pp. 4669 - 4678 (I.F. 6.6).
2016	A. Majumdar and R. K. Ward, Robust Dictionary Learning: Application to Signal Disaggregation IEEE ICASSP 2016.

BioInformatics

2023	Pooja Gupta, Angshul Majumdar, Emilie Chouzenoux and Giovanni Chierchia DeConDFFuse: Predicting Drug-Drug Interaction using joint Deep Convolutional Transform Learning and Decision Forest fusion framework , Expert Systems With Applications.
	Stuti Jain, Emilie Chouzenoux, Kriti Kumar and Angshul Majumdar, Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction , IEEE Journal of Biomedical and Health Informatics (J-BHI).
	Sarita Poonia, Anurag Goel, Smriti Chawla, Namrata Bhattacharya, Priyadarshini Rai, Yi Fang Lee, Yoon Sim Yap, Jay West, Ali Asgar Bhagat, Juhi Tayal, Anurag Mehta, Gaurav Ahuja, Angshul Majumdar, Naveen Ramalingam, and Debarka Sengupta, Marker-free characterization full-length transcriptomes of single live circulating tumor cell , Genome Research 33, no. 1 (2023) 80-95.
2022	A. Mongia, S. Jain, E. Chouzenoux and A. Majumdar, DeepVir - Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning: Application to COVID-19, Journal of Computational Biology.
2021	Aanchal Mongia, Angshul Majumdar and Emilie Chouzenoux, Computational prediction of Drug-Disease association based on Graph-regularized one bit Matrix completion , IEEE/ACM Transactions on Computational Biology and Bioinformatics.
	A. Mongia, S. K. Saha, E. Chouzenoux and A. Majumdar, A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials , Nature Scientific Reports.
2020	Rachesh Sharma, Neetesh Pandey, Aanchal Mongia, Shreya Mishra, Angshul Majumdar, Vibhor Kumar, FITs: Forest of imputation trees for recovering true signals in single-cell open chromatin profiles , NAR Genomcs and Bioinformatics.
	P. Rai, D. Sengupta and A. Majumdar, Cluster Aware Deep Dictionary Learning for Single Cell Analysis , ICONIP 2020.
	P. Rai, D. Sengupta and A. Majumdar, SelfE: Gene Selection via Self Expression for Single-Cell Tata , IEEE Transactions on Computational Biology and Bioinformatics (I.F. 2.8).
	A. Mongia and A. Majumdar, Drug-Target Interaction prediction using Multi Graph Regularized Nuclear Norm Minimization , PLOS ONE, vol. 15, no. 1, p.e0226484, 2020. (I.F. 2.7)
	A. Mongia, D. Sengupta and A. Majumdar, deepMC: deep Matrix Completion for imputation of single cell RNA-seq data , Journal of Computational Biology (accepted) (I.F. 1.2)
	A. Mongia and A. Majumdar, Deep Matrix Completion on Graphs: Application in Drug Target Interaction Prediction , IEEE ICASSP 2020.
2019	A. Mongia, D. Sengupta and A. Majumdar, McImpute: Matrix completion based imputation for single cell RNA-seq , Frontiers in Genetics, Vol. 10, 2019. (I.F. 4.1).
2018	D. Talwar, A. Mongia, D. Sengupta and A. Majumdar, AutoImpute: Autoencoder based imputation of single-cell RNA-seq data Nature Scientific Reports, vol. 8, no. 1, pp. 1-11, 2018 (I.F. 4.1).
	A. Mongia, V. Jain, E. Chouzenoux and A. Majumdar,, Deep Latent Factor Model for Predicting Drug Target Interactions , IEEE ICASSP, pp. 1254-1258, 2019.

Inverse Problems

2021	Angshul Majumdar, Solving Inverse Problems with Autoencoders on Learnt Graphs , Signal Processing.
	V. Singhal and A. Majumdar, A domain adaptation approach to solve inverse problems in imaging via coupled deep dictionary learning , Pattern Recognition (accepted), (I.F. 5.9)
	V. Singhal and A. Majumdar, Reconstructing Multi-echo Magnetic Resonance Images via Structured Deep Dictionary Learning , Neurocomputing (accepted), (I.F. 4.0)
2019	A. Majumdar, Blind Denoising Autoencoder, IEEE Transactions on Neural Networks and Learning Systems, Vol. 30 (1), pp. 312-317, 2019 (I.F. 7.9).
	S. Sugavanam, A. A. Gbadebo, M. Kamalian-Kopae, and A. Majumdar, A Compressed Sensing Approach to Fibre Bragg Interrogation , CLEO Europe 2019.
2018	D. J. Lewis, V. Singhal and A. Majumdar, Solving Inverse Problems in Imaging via Deep Dictionary Learning, IEEE Access, Vol. 7, 37039 - 37049 (I.F. 3.2).
	A. Majumdar, An Autoencoder Based Formulation for Compressed Sensing Reconstruction, Magnetic Resonance Imaging, Vol. 52, pp. 62-68, 2018 (I.F. 2.5).
	J. Maggu, P. Singh and A. Majumdar, Multi-echo Reconstruction from Partial K-space Scans via Adaptively Learnt Basis, Magnetic Resonance Imaging, Vol. 52, pp. 62-68, 2018 (I.F. 2.5).
	J. Lewis, V. Singhal and A. Majumdar, Adaptive Deep Dictionary Learning for MRI Reconstruction, ICONIP, pp. 3-11, 2018.
	K. Gupta, B. Bhowmick and A. Majumdar, Coupled Analysis Dictionary Learning to inductively learn inversion: Application to real-time reconstruction of Biomedical signals, IEEE IJCNN 2018.
	S. Viswakarma, S. S. Ram and A. Majumdar, Mitigation of Through-Wall Interference in Radar Images Using Denoising Autoencoders, IEEE RadarCon, 2018.
2017	A. Majumdar, Causal MRI Reconstruction via Kalman Prediction and Compressed Sensing Correction Magnetic Resonance Imaging, Vol. 39, pp. 64-70, 2017 (I.F. 2.2).
	J. Mehta and A. Majumdar, RODEO: Robust DE-aliasing autoencOder for Real-time Medical Image Reconstruction, Pattern Recognition, Vol. 63, pp. 499-510, 2017 (I.F. 3.3).
	W Singh, A Shukla, S Deb, A Majumdar, Energy Efficient EEG Acquisition and Reconstruction for a Wireless Body Area Network, Integration, the VLSI Journal, Vol. 58, pp. 295-302, 2017.
	J. Maggu, R. Hussein, A. Majumdar and R. Ward, Impulse Denoising via Transform Learning IEEE GlobalSIP, pp. 1250-1254, 2017.
	P. Singh, R. Hussein, A. Majumdar and R. Ward, Joint-sparse Dictionary Learning: Denoising Multiple Measurement Vectors IEEE GlobalSIP, 2017.
	K. Gupta, B. Biswas and A. Majumdar, Motion Blur Removal via Coupled Autoencoder IEEE ICIP, 2017.
2016	A. Majumdar and R. K. Ward, Real-time Reconstruction of EEG Signals from Compressive Measurements via Deep Learning IEEE IJCNN 2016.
	S. S. Ram and A. Majumdar, Through-wall Propagation Effects on Doppler-enhanced Frontal Radar Images of Humans IEEE RadarCon, 2016
	A. Gogna and A. Majumdar, Fast Acquisition for Quantitative MRI Maps: Sparse Recovery from Non-linear Measurements Data Compression Conference.
	K. Gupta, A. Raj and A. Majumdar, Analysis and Synthesis Prior Greedy Algorithms for Non-linear Sparse Recovery Data Compression Conference.
2015	P. Khurana, P. Bhattacharjee and A. Majumdar, Matrix Factorization from Non-linear Projections: Application in Estimating T2 Maps from Few Echoes, Magnetic Resonance Imaging, Vol. 33 (7), pp. 927-931, 2015. (I.F. 2.0).
	A. Majumdar and R. K. Ward, Energy Efficient EEG Sensing and Transmission for Wireless Body Area Networks: A Blind Compressed Sensing Approach, Biomedical Signal Processing and Control, Vol. 20, pp. 1-9, 2015. (I.F. 1.5).
	A. Shukla and A. Majumdar, Exploiting Inter-channel Correlation in EEG Signal Reconstruction, Biomedical Signal Processing and Control, Vol. 18 (4), pp. 49–55, 2015 (I.F. 1.5).
	S. S. Ram and A. Majumdar, High-resolution radar imaging of moving humans using doppler processing and compressed sensing, IEEE Transactions on Aerospace and Electronic Systems, Vol. 51, pp. 1279-1287, 2015 (I.F. 1.3).
	A. Shukla and A. Majumdar, Row-sparse Blind Compressed Sensing for Reconstructing Multi-channel EEG signals, Biomedical Signal Processing and Control, Vol. 18 (4), pp. 174–178, 2015 (I.F. 1.5).
	A. Majumdar, Improving Synthesis and Analysis Prior Blind Compressed Sensing with Low-rank Constraints for Dynamic MRI Reconstruction, Magnetic Resonance Imaging, Vol. 33(1), pp. 174-179, 2015 (I.F. 2.0).
	A. Majumdar and R. K. Ward, Learning Space-Time Dictionaries for Blind Compressed Sensing Dynamic MRI Reconstruction IEEE ICIP 2015.
	P. Bhattacharjee, P. Khurana and A. Majumdar, Low-rank Matrix Recovery from Non-linear Observations 20th IEEE DSP 2015.
	A. Majumdar and R. K. Ward Learning the Sparsity Basis in Low-rank plus Sparse Model for Dynamic MRI Reconstruction ICASSP 2015
	A. Majumdar, A. Shukla and R. K. Ward, Combining Sparsity with Rank-Deficiency for Energy Efficient EEG Sensing and Transmission over Wireless Body Area Network ICASSP 2015
	A. Gogna and A. Majumdar, Blind Compressive Sensing Framework For Collaborative Filtering ICASSP 2015
2014	A. Majumdar and R. Ward, Exploiting Sparsity and Rank Deficiency for MR Image Reconstruction from Multiple Partial K-Space Scans, IEEE Canadian Journal of Electrical and Computer Engineering, Vol. 37 (4), pp. 228, 235, 2014 (invited).
	A. Majumdar, A. Gogna and R. Ward, Low-rank Matrix Recovery Approach For Energy Efficient EEG Acquisition for Wireless Body Area Network, Sensors, Special Issue on State-of-the-art Sensor Technologies in Canada, Vol. 14(9), pp. 15729-15748, 2014 (I.F. 2.0).
	A. Majumdar and R. K. Ward, Non-Convex Row-sparse MMV Analysis Prior Formulation For EEG Signal Reconstruction, Biomedical Signal Processing and Control, Vol. 13, pp. 142–147, 2014 (I.F. 1.5).
	P. Das, M. Jain and A. Majumdar, Non Linear Sparse Recovery Algorithm, IEEE ISSPIT 2014.
	A. Rajani, P. Mittal, A. Jain and A. Majumdar, A Blind Compressed Sensing Formulation for Collaborative Filtering, IEEE ISSPIT 2014.
	A. Shah and A. Majumdar, Sparse Recovery on GPUs: Accelerating the Iterative Soft-Thresholding Algorithm, IEEE ISSPIT 2014.
	H. Agarwal and A. Majumdar, Generalized Synthesis and Analysis Prior Algorithms with Application to Impulse Denoising, ICVGIP 2014.
	W. Singh, A. Shukla, S. Deb and A. Majumdar, Energy Efficient Acquisition and Reconstruction of EEG Signals, IEEE EMBC 2014.
	A. Shah and A. Majumdar, Parallelizing Sparse Recovery Algorithms: A Stochastic Approach, 19th IEEE DSP.
	H. K. Agarwal and A. Majumdar, Extension of Sparse Randomized Kaczmarz Algorithm for Multiple Measurement Vectors, International Conference on Pattern Recognition, 2014.
	A. Gogna, A. Shukla and A. Majumdar, Matrix Recovery using Split Bregman, International Conference on Pattern Recognition, 2014.
	A. Majumdar and R. K. Ward, Improved Blind Compressed Sensing for Dynamic MRI Reconstruction, ISMRM 2014.
	A. Majumdar and R. K. Ward, Elastic Net Formulation for MRI Reconstruction, ISMRM 2014.
	A. Majumdar and R. K. Ward, Improved MRI Reconstruction via Non-Convex Elastic Net, ICASSP 2014.
	A. Majumdar and S. S. Ram, Two-Dimensional Array Processing with Compressed Sensing, RadarCon 2014.
2013	A. Majumdar, K. Chaudhury and R. Ward, Calibrationless Parallel Magnetic Resonance Imaging: A Joint Sparsity Model, Sensors, Special Issue on Magnetic Resonance Sensors, Vol. 13(12), pp. 16714-16735, 2013. (I.F. 2.0)
	M. Mohsina and A. Majumdar, Gabor Based Analysis Prior Formulation For EEG Signal Reconstruction, Biomedical Signal Processing and Control, Vol. 8 (6), pp. 951–955, 2013 (I.F. 1.5).
	A. Majumdar, Motion Predicted Online Dynamic MRI Reconstruction from Partially Sampled K-Space Data, Magnetic Resonance Imaging, Vol. 31 (9), pp. 1578–1586, 2013. (I.F. 2.0)
	H. S. Chen, A. Majumdar and P. Kozlowski, Compressed Sensing CPMG with Group-Sparse Reconstruction for Myelin Water Imaging, Magnetic Resonance in Medicine, Vol. 71 (3), pp. 1166-1171, 2013, (I. F. 3.0).
	A. Majumdar and R. K. Ward, Rank Awareness in Group-sparse Recovery of Multi-echo MR Images, Sensors, Special Issue on Medical and Biomedical Imaging, Vol. 13 (3), pp. 3902-3921, 2013. (I.F. 2.0)
	A. Majumdar, Improved Dynamic MRI Reconstruction by Exploiting Sparsity and Rank-Deficiency, Magnetic Resonance Imaging, Vol. 31(5), pp. 789-95, 2013. (I.F. 2.0)
	A. Majumdar and R. K. Ward, Dynamic CT Reconstruction by Smoothed Rank Minimization, MICCAI, Vol. 8151, pp 131-138, 2013.
	A. Majumdar, R. K. Ward and T. Aboulnasr, FOCUSS Algorithm for Rank-Aware Row Sparse MMV Recovery, EUSIPCO 2013.
	A. Majumdar and R. K. Ward, Exploiting Sparsity and Rank-deficiency in Dynamic MRI Reconstruction, IEEE ICASSP, 2013.
	A. Majumdar and R. K. Ward, Exploiting Rank Deficiency for MR Image Reconstruction from Multiple Partial K-Space Scans, IEEE CCECE 2013.

Remote Sensing

2019	J. Maggu, H. Agarwal and A. Majumdar, Label Consistent Transform Learning for Hyperspectral Image Classification , IEEE Geosciences and Remote Sensing Letters, Vol. 16 (9), pp. 1502-1506, 2019 (I.F. 2.9).
	V. Singhal and A. Majumdar, Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11 (12), 5019 – 5028, 2019 (I.F. 2.7).
2017	V. Singhal, H. Agrawal, S. Tariyal and A. Majumdar, Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification IEEE Transactions on Geosciences and Remote Sensing, Vol. 55 (9), pp. 5274-5283, 2017. (I.F. 4.9).
2016	S. Tariyal, H. Agrawal and A. Majumdar, Removing Sparse Noise from Hyperspectral Images with Sparse and Low-rank Penalties, SPIE Journal of Electronic Imaging (accepted) (I.F. 0.7).
	H. Agrawal and A. Majumdar, Hyperspectral Unmixing in the Presence of Mixed Noise using Joint-Sparsity and Total-Variation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9 (9), pp. 4257 – 4266, 2016. (I.F. 3.0).
	N. Kohli, M. Vatsa, R. Singh, A. Noore and A. Majumdar, Hyperspectral Image Denoising Using Spatio-Spectral Total Variation, IEEE Geosciences and Remote Sensing Letters, Vol. 13 (3), pp. 442-446, 2016. (I.F. 2.2).
	A. Majumdar, N. Ansari, H. Agarwal and P. Biyani, Impulse Denoising for Hyper-Spectral Images: A Blind Compressed Sensing Approach, Signal Processing, Vol. 119, pp. 136-141, 2016. (I.F. 2.2)
	S. Tariyal, H. K. Aggarwal and A. Majumdar, Greedy Deep Dictionary Learning for Hyperspectral Image Classification IEEE WHISPERS 2016.
	H. K. Aggarwal and A. Majumdar, Sparse Filtering based Hyperspectral Unmixing IEEE WHISPERS 2016.
	H. K. Aggarwal and A. Majumdar, Compressive Hyper-Spectral Imaging in the Presence of Real Noise IEEE IGARSS 2016.
2015	H. Aggarwal and A. Majumdar, Exploiting Spatio-Spectral Correlation for Impulse Denoising in Hyperspectral Images, SPIE Journal of Electronic Imaging, Vol. 24(1), 013027, 2015 (I.F. 0.7).
	H. K. Agrawal and A. Majumdar, Blind Hyperspectral Denoising NCVPRIPG 2015.
	H. K. Aggarwal and A. Majumdar, Blind Compressive Hyper-Spectral Imaging IEEE IGARSS 2015
	H. K. Aggarwal and A. Majumdar, Mixed Gaussian and Impulse Denoising of Hyperspectral Images IEEE IGARSS 2015
	S. Tariyal, H. K. Aggarwal and A. Majumdar, Hyperspectral Impulse Denoising with Sparse and Low-Rank Penalties IEEE WHISPERS 2015.
	H. K. Aggarwal, S. Tariyal and A. Majumdar, Compressive Hyper-Spectral Imaging in The Presence of Impulse Noise IEEE WHISPERS 2015.
	A. Majumdar, N. Ansari and H. Aggarwal, Hyper-spectral Impulse Denoising: A row-sparse Blind Compressed Sensing Formulation ICASSP 2015
2014	A. Gogna, A. Shukla, H. Agarwal and A. Majumdar, Split Bregman Algorithms for Sparse / Joint-sparse and Low-rank Signal Recovery: Application in Compressive Hyperspectral Imaging, IEEE ICIP 2014.
	H. K. Agarwal and A. Majumdar, Single-Sensor Multi-Spectral Image Demosaicing Algorithm Using Learned Interpolation Weights, IEEE IGARSS 2014.

Recommender Systems

2020	A. Mongia, N. Jhamb, E. Chouzenoux and A. Majumdar, Deep Latent Factor Model for Collaborative Filtering , Signal Processing, Vol. 169, 107366, 2020 (I.F. 4.0)
2019	A. Mongia and A. Majumdar, Matrix Completion on Multiple Graphs: Application in Collaborative Filtering , Signal Processing, Vol. 165, pp. 144-148, 2019. (I.F. 4.0).
2018	S. Jain and A. Majumdar, Doubly Label Consistent Autoencoder: Accounting User and Item Metadata in Recommender Systems, IEEE IJCNN 2018.
	S. Maheshwari and A. Majumdar, Hierarchical Autoencoder for Collaborative Filtering, IEEE IJCNN 2018.
2017	A. Gogna and A. Majumdar, Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework, Knowledge Based Systems, Vol. 125, pp. 83-95, 2017. (I.F. 4.5).
	A. Gogna and A. Majumdar, DiABlO: Optimization based design for improving diversity in recommender system Information Sciences, Vol. 378, pp. 59-74, 2017 (I.F. 4.8).
	A. Jain and A. Majumdar, Cold-start, Warm-start and Everything in Between: An Autoencoder based Approach to Recommendation IEEE IJCNN, 2017.
2016	A. Gogna and A. Majumdar, Supervised Learning in Matrix Completion Framework for Recommender System Design COMAD 2016.
2015	A. Gogna and A. Majumdar, A Comprehensive Recommender System Model: Improving Accuracy for both Warm and Cold Start Users, IEEE ACESS, Vol. 2803 – 2813 (I.F. 1.2).
	A. Gogna and A. Majumdar, Blind Compressive Sensing Formulation Incorporating Metadata for Recommender System Design, APSIPA Transactions on Signal and Information Processing (Cambridge Journal), Vol. 4, 2015.
	A. Gogna and A. Majumdar, Matrix Completion Incorporating Auxiliary Information for Recommender System Design, Expert Systems with Applications, Vol. 24 (14), pp. 5789-5799, 2015. (I.F. 2.9).
2014	A. Gogna and A. Majumdar, Distributed Elastic Net Regularized Blind Compressive Sensing for Recommender System Design, COMAD 2014.

Miscellaneous

2020	A. Majumdar, Graph Transform Learning , Neural Networks, Vol. 128, pp. 248-253, 2020. (I.F. 5.7)
	J. Maggu, A. Majumdar and E. Chouzenoux, Transformed Subspace Clustering , IEEE Transactions on Knowledge and Data Engineering (accepted), (I.F. 4.0)
	A. Pal, A. Ukil, T. Deb, I. Sahu and A. Majumdar, Instant Adaptive Learning: An Adaptive Filter Based Fast Learning Model Construction for Sensor Signal Time Series Classification on Edge Devices , IEEE ICASSP 2020.
2019	J. Maggu and A. Majumdar, Supervised Kernel Transform Learning , IEEE IJCNN 2019.
2018	J. Maggu and A. Majumdar, Semi-Coupled Transform Learning, ICONIP, pp. 141-150, 2018.
	J. Maggu, A. Majumdar and E. Chouzenoux, Transformed Locally Linear Manifold Clustering, EUSIPCO, pp. 1057-1061, 2018.
	K. Kumar, A. Majumdar, G. Chandra and A. A. Kumar, Regressing Kernel Dictionary Learning, IEEE ICASSP, 2018.
2017	J. Maggu and A. Majumdar, Kernel Transform Learning, Pattern Recognition Letters, Vol. 117, pp. 117-122, 2017 (I.F. 1.9).
	G. Goswami, M. Vatsa, R. Singh and A. Majumdar, Kernel Group Sparse Representation based Classifier for Multimodal Biometrics IEEE IJCNN, 2017.
	P. Bhattacharjee, S. Banerjee, M. Gulati, A. Majumdar and S. S. Ram, Supervised Analysis Dictionary Learning: Application in Consumer Electronics Appliance Classification CODS, 2017.
	J. Maggu and A. Majumdar, Robust Transform Learning IEEE ICASSP, pp. 1467-1471, 2017
2016	G. Goswami, P. Mittal, A. Majumdar, R. Singh and M. Vatsa, Group Sparse Representation based Classification for Multi-feature Multimodal Biometrics, Information Fusion, Vol. 32 (B), pp. 3 - 12. (I.F. 10.7)
	J. Maggu and A. Majumdar, Alternate Formulation for Transform Learning ICVGIP, pp. 501-508, 2016.
	A. Gogna and A. Majumdar, Kernel l1-minimization: Application to Kernel Sparse Representation based Classification ICONIP, pp. 136-143, 2016.
	A. Gogna and A. Majumdar, Nuclear Norm Regularized Randomized Neural Network ICONIP, pp. 144-151, 2016.
	S. Yadav, M. Singh, M. Vatsa, R. Singh and A. Majumdar Low Rank Group Sparse Representation Based Classifier for Pose Variation IEEE ICIP 2016.
	H. K. Agrawal and A. Majumdar, Robust Estimation for Subspace Based Classifiers IEEE IJCNN 2016.
	M. Jain, N. Kumar, S. Deb and A. Majumdar, A Sparse Regression based Approach for Cuff-less Blood Pressure Measurement IEEE ICASSP 2016.
2015	A. Majumdar, Discriminative Label Consistent Dictionary Learning IEEE ICIP 2015.
	A. Majumdar, Frontal Face Recognition from Video via Rank-Aware Multiple Measurement Vector Recovery 20th IEEE DSP 2015.
	A. Gogna and A. Majumdar, Matrix Factorization Model using Kacmarz Algorithm: Application in Sensor Localization 20th IEEE DSP 2015. Best Paper Award
2013	A. Majumdar, R. K. Ward and T. Aboulnasr, Generalized Non-Linear Sparse Classifier, EUSIPCO 2013.
	A. Majumdar and R. K. Ward, Non-linear Sparse and Group-sparse Classifier”, IEEE CCECE 2013.