The sparse data structure represents a matrix in space proportional to the number of non-zero entries. Many storage formats have been proposed to represent sparse matrices. In this paper we evaluate and compare the storage efficiency of various sparse matrix storage formats, and consider the performance results of matrix-vector multiplication using these storage formats.

A model for localized key finding from audio is proposed. Besides being able to estimate the key in which a piece starts, the model can also identify points of modulation and label multiple sections with their key names throughout a single piece. The front-end employs an adaptive tuning stage prior to spectral analysis and calculation of chroma features. The segmentation stage uses groups of co...

Compressed sensing (CS) states that a sparse signal can exactly be recovered from very few linear measurements. While in many applications, real-world signals also exhibit additional structures aside from standard sparsity. The typical example is the so-called block-sparse signals whose non-zero coefficients occur in a few blocks. In this article, we investigate the mixed l2/lq(0 < q ≤ 1) norm ...

Non-stationary prediction-error filters have previously been used to interpolate sparse, regularly sampled data. I take an existing method used to estimate a stationary predictionerror filter on sparse, irregularly sampled data, and extend it to use non-stationary prediction-error filters. I then apply this method to interpolate a non-stationary test case, with promising results. I also examine...

In this paper, a new algorithm for estimating sparse non-negative sources from a set of noisy linear mixtures is proposed. In particular, difficult situations with high noise levels and more sources than sensors (underdetermined case) are considered. It is shown that, when sources are very sparse in time and overlapped at some locations, they can be recovered even with very low SNR and by using...

We consider the problem of classification using high dimensional features’ space. In a paper by Bickel and Levina (2004), it is recommended to use naive-Bayes classifiers, that is, to treat the features as if they are statistically independent. Consider now a sparse setup, where only a few of the features are informative for classification. Fan and Fan (2008), suggested a variable selection and...

In this paper, we introduce a new detection algorithm for large-scale wireless systems, referred to as post sparse error detection (PSED) algorithm, that employs a sparse error recovery algorithm to refine the estimate of a symbol vector obtained by the conventional linear detector. The PSED algorithm operates in two steps: 1) sparse transformation converting the original non-sparse system into...

PROXTONE is a novel and fast method for optimization of large scale non-smooth convex problem [18]. In this work, we try to use PROXTONE method in solving large scale non-smooth non-convex problems, for example training of sparse deep neural network (sparse DNN) or sparse convolutional neural network (sparse CNN) for embedded or mobile device. PROXTONE converges much faster than first order met...

Combining information from various image descriptors has become a standard technique for image classification tasks. Multiple kernel learning (MKL) approaches allow to determine the optimal combination of such similarity matrices and the optimal classifier simultaneously. Most MKL approaches employ an `-regularization on the mixing coefficients to promote sparse solutions; an assumption that is...