Sparse coding has been a popular learning model in machine learning field. However, due to the complexity of the learning model, the high computational cost has seriously hindered its application. Toward this purpose, this paper presents a parallel sparse coding method to improve the performance by exploiting the power of acceleration technologies such as Intel MIC and GPU. We use both parallel...

Sparse coding which encodes the original signal in a sparse signal space, has shown its state-of-the-art performance in the visual codebook generation and feature quantization process of BoW based image representation. However, in the feature quantization process of sparse coding, some similar local features may be quantized into different visual words of the codebook due to the sensitiveness o...

The goal in sparse coding is to seek a linear basis representation where each image is represented by a small number of active coefficients. The learning algorithm involves adapting a basis vector set while imposing a low-entropy, or sparse, prior on the output coefficients. Sparse coding applied on natural images has been shown to extract wavelet-like structure [9, 4]. However, our experience ...

We propose in this paper a novel sparse subspace clustering method that regularizes sparse subspace representation by exploiting the structural sharing between tasks and data points via group sparse coding. We derive simple, provably convergent, and computationally efficient algorithms for solving the proposed group formulations. We demonstrate the advantage of the framework on three challengin...

This paper investigates a mechanism for reliable generation of sparse code in a sparsely connected, hierarchical, learning memory. Activity reduction is accomplished with local competitions that suppress activities of unselected neurons so that costly global competition is avoided. The learning ability and the memory characteristics of the proposed winner-take-all network and an oligarchy-take-...

We present two new methods which extend the traditional sparse coding approach with supervised components. The goal of these extensions is to increase the suitability of the learned features for classification tasks while keeping most of their general representation performance. A special visualization is introduced which allows to show the principal effect of the new methods. Furthermore some ...

A number of researchers have theorized that the brain may be employing some form of hierarchical model of features in visual processing. Nodes at the bottom of the hierarchy would represent local, spacially-oriented, specific features, while levels further up the hierarchy would detect increasingly complex, spatially-diffuse, and invariant features, with nodes in the uppermost layers correspond...

We consider the problem of building shift-invariant representations for long signals in the context of distributed processing. We propose an asynchronous algorithm based on coordinate descent called DICOD to efficiently solve the `1minimization problems involved in convolutional sparse coding. This algorithm leverages the weak temporal dependency of the convolution to reduce the interprocess co...

We present sparse topical coding (STC), a non-probabilistic formulation of topic models for discovering latent representations of large collections of data. Unlike probabilistic topic models, STC relaxes the normalization constraint of admixture proportions and the constraint of defining a normalized likelihood function. Such relaxations make STC amenable to: 1) directly control the sparsity of...

Bag-of-words document representations are often used in text, image and video processing. While it is relatively easy to determine a suitable word dictionary for text documents, there is no simple mapping from raw images or videos to dictionary terms. The classical approach builds a dictionary using vector quantization over a large set of useful visual descriptors extracted from a training set,...