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 ...

Convolutional sparse coding (CSC) improves sparse coding by learning a shift-invariant dictionary from the data. However, existing CSC algorithms operate in the batch mode and are expensive, in terms of both space and time, on large data sets. In this paper, we alleviate these problems by using online learning. The key is a reformulation of the CSC objective so that convolution can be handled e...

Spoken language recognition requires a series of signal processing steps and learning algorithms to model distinguishing characteristics of different languages. In this paper, we present a sparse discriminative feature learning framework for language recognition. We use sparse coding, an unsupervised method, to compute efficient representations for spectral features from a speech utterance whil...

Sparse coding is a crucial subroutine in algorithms for various signal processing, deep learning, and other machine learning applications. The central goal is to learn an overcomplete dictionary that can sparsely represent a given dataset. However, storage, transmission, and processing of the learned dictionary can be untenably high if the data dimension is high. In this paper, we consider the ...