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.

Crowdsourcing utilizes human ability by distributing tasks to a large number of workers. It is especially suitable for solving data clustering problems because it provides a way to obtain a similarity measure between objects based on manual annotations, which capture the human perception of similarity among objects. This is in contrast to most clustering algorithms that face the challenge of fi...

This article presents an information retrieval system (IRS) using genetic algorithm to increase the performance and efficiency of Text to Matrix Generator (TMG).This paper presents an extension in previous work of Text to Matrix Generator (TMG). In this paper, we proposed a genetic algorithm approach in Text to Matrix Generator for information retrieval. This experimental result shows an improv...

We have developed an approximate signal recovery algorithm with low computational cost for compressed sensing on the basis of randomly constructed sparse measurement matrices. The law of large numbers and the central limit theorem suggest that the developed algorithm saturates the Donoho-Tanner weak threshold for the perfect recovery when the matrix becomes as dense as the signal size N and the...

For a self-adjoint linear operator with discrete spectrum or a Hermitian matrix the “extreme” eigenvalues define the boundaries of clusters in the spectrum of real eigenvalues. The outer extreme ones are the largest and the smallest eigenvalues. If there are extended intervals in the spectrum in which no eigenvalues are present, the eigenvalues bounding these gaps are the inner extreme eigenval...

Geophysical inverse problems typically involve a trade off between data misfit and some prior. Pareto curves trace the optimal trade off between these two competing aims. These curves are commonly used in problems with two-norm priors where they are plotted on a log-log scale and are known as L-curves. For other priors, such as the sparsity-promoting one norm, Pareto curves remain relatively un...

Existing frequent subgraph mining algorithms can operate efficiently on graphs that are sparse, have vertices with low and bounded degrees, and contain welllabeled vertices and edges. However, there are a number of applications that lead to graphs that do not share these characteristics, for which these algorithms highly become inefficient. In this paper we propose a fast algorithm for mining f...

The use of the second order statistical measures has became popular in the image database indexing and retrieval. Unlike the common approach, image histogram, second order statistics like image correlogram and autocorrelogram consider also the spatial organization of the image colors or gray levels. Recently, correlograms and autocorrelograms have been widely used in the image database indexing...

Computing a hierarchical clustering of objects from a pairwise distance matrix is an important algorithmic kernel in computational science. Since the storage of this matrix requires quadratic space with respect to the number of objects, the design of memory-efficient approaches is of high importance to this research area. In this paper, we address this problem by presenting a memory-efficient o...