Sparse representation has become very popular in fields of signal processing, image processing computer vision and pattern recognition. Sparse representation also has good reputation in both theoretical and practical applications. Images can be sparsely coded by structural primitives and recently the sparse coding or sparse representation has been widely used to resolve the problems in image re...

In this paper, we proposed a novel sparse coding algorithm by using the class labels to constrain the learning of codebook and sparse code. We not only use the class label to train the classifier, but also use it to construct class conditional codewords to make the sparse code as discriminative as possible. We first construct ideal sparse codes with regarding to the class conditional codewords,...

Sparse coding has recently become a popular approach in computer vision to learn dictionaries of natural images. In this paper we extend the sparse coding framework to learn interpretable spatio-temporal primitives. We formulated the problem as a tensor factorization problem with tensor group norm constraints over the primitives, diagonal constraints on the activations that provide interpretabi...

Sparse coding networks, which utilize unsupervised learning to maximize coding efficiency, have successfully reproduced response properties found in primary visual cortex [1]. However, conventional sparse coding models require that the coding circuit can fully sample the sensory data in a one-to-one fashion, a requirement not supported by experimental data from the thalamo-cortical projection. ...

Efficient codes have been shown to perform well in image and audio classification tasks, but the impact of sparsity—and indeed the entire notion of efficient coding—has not yet been well explored in the context of human movements. This paper tests several coding approaches on a movement classification task and finds that efficient codes for kinematic (joint angle) data perform well for classify...

Two classes of relatively simple algorithms have been found to be very effective for unsupervised feature learning: 1) sparse coding that minimizes the reconstruction error, and 2) clustering that captures the data distribution. Coates et al. (2011) analyzed the performance of several off-the-shelf feature learning algorithms, such as, sparse auto-encoders, sparse RBMs, k-means clustering, and ...

This thesis deals with developing improved techniques for speech coding based on the recent developments in sparse signal representation. In particular, this work is motivated by the need to address some of the limitations of the wellknown linear prediction (LP) model currently applied in many modern speech coders. In the first part of the thesis, we provide an overview of Sparse Linear Predict...

In this paper, we explore an efficient variant of convolutional sparse coding with unit norm code vectors where reconstruction quality is evaluated using an inner product (cosine distance). To use these codes for discriminative classification, we describe a model we term Energy-Based Spherical Sparse Coding (EB-SSC) in which the hypothesized class label introduces a learned linear bias into the...

Mass spectrometry is an important technique for chemical profiling and is a major tool in proteomics, a discipline interested in large-scale studies of proteins expressed by an organism. In this paper we propose using a sparse coding algorithm for classification of mass spectrometry serum protein profiles of colorectal cancer patients and healthy individuals following the so-called self-taught ...