81 citations · 168 across the 67 of their papers we have counts for
23 papers · 1 filter
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework
Anupriya Gogna, Angshul Majumdar
Design of recommender systems aimed at achieving high prediction accuracy is a widely researched area. However, several studies have suggested the need for diversified recommendati…
Discriminative Autoencoder for Feature Extraction: Application to Character Recognition
Anupriya Gogna, Angshul Majumdar
Conventionally, autoencoders are unsupervised representation learning tools. In this work, we propose a novel discriminative autoencoder. Use of supervised discriminative learning…
Analysis Co-Sparse Coding for Energy Disaggregation
Shikha Singh, Angshul Majumdar
Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This i…
Kernel Transform Learning
Jyoti Maggu, Angshul Majumdar
This work proposes kernel transform learning. The idea of dictionary learning is well known; it is a synthesis formulation where a basis is learnt along with the coefficients so as…
Deep Sparse Coding for Non-Intrusive Load Monitoring
Shikha Singh, Angshul Majumdar
Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This i…
Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals
Anupriya Gogna, Angshul Majumdar, Rabab Ward
In this work we propose an autoencoder based framework for simultaneous reconstruction and classification of biomedical signals. Previously these two tasks, reconstruction and clas…
Label Consistent Transform Learning for Hyperspectral Image Classification
Jyoti Maggu, Hemant K. Aggarwal, Angshul Majumdar
This work proposes a new image analysis tool called Label Consistent Transform Learning (LCTL). Transform learning is a recent unsupervised representation learning approach; we add…
Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification
Vanika Singhal, Angshul Majumdar
In recent studies in hyperspectral imaging, biometrics and energy analytics, the framework of deep dictionary learning has shown promise. Deep dictionary learning outperforms other…
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification
Vanika Singhal, Hemant K. Aggarwal, Snigdha Tariyal +1
This work proposes a new framework for deep learning that has been particularly tailored for hyperspectral image classification. We learn multiple levels of dictionaries in a robus…
Majorization Minimization Technique for Optimally Solving Deep Dictionary Learning
Vanika Singhal, Angshul Majumdar
The concept of deep dictionary learning has been recently proposed. Unlike shallow dictionary learning which learns single level of dictionary to represent the data, it uses multip…
Simultaneous Detection of Multiple Appliances from Smart-meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning
Vanika Singhal, Jyoti Maggu, Angshul Majumdar
Currently there are several well-known approaches to non-intrusive appliance load monitoring rule based, stochastic finite state machines, neural networks and sparse coding. Recent…
RODEO: Robust DE-aliasing autoencOder for Real-time Medical Image Reconstruction
Janki Mehta, Angshul Majumdar
In this work we address the problem of real-time dynamic medical MRI and X Ray CT image reconstruction from parsimonious samples Fourier frequency space for MRI and sinogram tomogr…
Non-intrusive Load Monitoring via Multi-label Sparse Representation based Classification
Shikha Singh, Angshul Majumdar
This work follows the approach of multi-label classification for non-intrusive load monitoring (NILM). We modify the popular sparse representation based classification (SRC) approa…
Blind Denoising Autoencoder
Angshul Majumdar
The term blind denoising refers to the fact that the basis used for denoising is learnt from the noisy sample itself during denoising. Dictionary learning and transform learning ba…
Increasing Energy Efficiency in Sensor Networks: Blue Noise Sampling and Non-Convex Matrix Completion
Angshul Majumdar, Rabab Ward
The energy cost of a sensor network is dominated by the data acquisition and communication cost of individual sensors. At each sampling instant it is unnecessary to sample and comm…
Multi-echo Reconstruction from Partial K-space Scans via Adaptively Learnt Basis
Jyoti Maggu, Prerna Singh, Angshul Majumdar
In multi echo imaging, multiple T1/T2 weighted images of the same cross section is acquired. Acquiring multiple scans is time consuming. In order to accelerate, compressed sensing…
Impulse Denoising From Hyper-Spectral Images: A Blind Compressed Sensing Approach
Angshul Majumdar, Naushad Ansari, Hemant Aggarwal +1
In this work we propose a technique to remove sparse impulse noise from hyperspectral images. Our algorithm accounts for the spatial redundancy and spectral correlation of such ima…
Recurrent Transform Learning
Megha Gupta, Angshul Majumdar
The objective of this work is to improve the accuracy of building demand forecasting. This is a more challenging task than grid level forecasting. For the said purpose, we develop…
Deep Latent Factor Model for Collaborative Filtering
Aanchal Mongia, Neha Jhamb, Emilie Chouzenoux +1
Latent factor models have been used widely in collaborative filtering based recommender systems. In recent years, deep learning has been successful in solving a wide variety of mac…
Transformed Subspace Clustering
Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux
Subspace clustering assumes that the data is sepa-rable into separate subspaces. Such a simple as-sumption, does not always hold. We assume that, even if the raw data is not separa…
Reconstructing Multi-echo Magnetic Resonance Images via Structured Deep Dictionary Learning
Vanika Singhal, Angshul Majumdar
Multi-echo magnetic resonance (MR) images are acquired by changing the echo times (for T2 weighted) or relaxation times (for T1 weighted) of scans. The resulting (multi-echo) image…
Multi Label Restricted Boltzmann Machine for Non-Intrusive Load Monitoring
Sagar Verma, Shikha Singh, Angshul Majumdar
Increasing population indicates that energy demands need to be managed in the residential sector. Prior studies have reflected that the customers tend to reduce a significant amoun…
Collaborative Filtering with Label Consistent Restricted Boltzmann Machine
Sagar Verma, Prince Patel, Angshul Majumdar
The possibility of employing restricted Boltzmann machine (RBM) for collaborative filtering has been known for about a decade. However, there has been hardly any work on this topic…