In recent years, sparse signal modeling, especially using the synthesis model has been
popular. Sparse coding in the synthesis model is however, NP-hard. Recently, interest has …

Currently there are several well-known approaches to non-intrusive appliance load
monitoring-rule based, stochastic finite state machines, neural networks, and sparse coding …

The development of computed tomography (CT) image reconstruction methods that
significantly reduce patient radiation exposure, while maintaining high image quality is an …

Techniques exploiting the sparsity of signals in a transform domain or dictionary have been
popular in signal processing. Adaptive synthesis dictionaries have been shown to be useful …

Compressed sensing is a powerful tool in applications such as magnetic resonance imaging
(MRI). It enables accurate recovery of images from highly undersampled measurements by …

The naive assumption of subspace clustering is that the data should be separable into
separate subspaces. Another consideration of the conventional subspace clustering …

This paper addresses the problem of energy disaggregation/non-intrusive load monitoring. It
introduces a new method based on the transform learning formulation. Several recent …

Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal
from an under-determined system of linear equations. The topic is of immense practical …

Exploiting the sparsity of signals in an adaptive dictionary or transform domain benefits
various applications in image/video processing. As opposed to synthesis dictionary learning …

As a widely-used method in the image categorization tasks, the Bag-of-Words (BoW) method
still suffers from many limitations such as overlooking spatial information. In this paper, we …