SIGNAL ANALYSIS FOR LARGE SCALE APPLICATIONS
2021 |
S. Sharma, V. Elvira, E. Chouzenoux and A. Majumdar, Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting , Neurocomputing (Accepted). |
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 (accepted). | |
Shalini Sharma and Angshul Majumdar, Sequential Transform Learning , Transactions on Knowledge Discovery from Data (accepted). | |
2020 |
Pooja Gupta, Angshul Majumdar, E. Chouzenoux and G. Chierchia, SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems , Expert Systems With Applications (accepted). |
J. Maggu, E. Chouzenoux, G. Chierchia and A. Majumdar, Deep Convolutional Transform Learning , ICONIP 2020 (accepted) | |
A. Mongia and A. Majumdar, Deep Matrix Factorization on Graphs: Application to Collaborative Filtering , ICONIP 2020 (accepted). | |
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. |
2020 |
S. Sharma, A. Majumdar, Unsupervised Detection of Non-Technical Losses via Recursive Transform Learning , IEEE Transactions on Power Delivery (accepted). |
S. Singh, A. Majumdar and S. Makonin, Compressive Non-Intrusive Load Monitoring , BuildSys'20, short paper (accepted). | |
S. Sharma, A. Majumdar, V. Elvira and E. Chouzenoux, Blind Kalman Filtering for Short-term Load Forecasting , IEEE Transactions on Power Systems (accepted). | |
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. |
2021 |
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 (Accepted). |
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 (accepted). |
P. Rai, D. Sengupta and A. Majumdar, Cluster Aware Deep Dictionary Learning for Single Cell Analysis , ICONIP 2020 (accepted). | |
P. Rai, D. Sengupta and A. Majumdar, SelfE: Gene Selection via Self Expression for Single-Cell Tata , IEEE Transactions on Computational Biology and Bioinformatics (accepted) (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. |
2020 |
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. |
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. |
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. |