Data mining technologies have now been used in commercial, industrial, and governmental businesses, for various purposes, ranging from increasing profitability to enhancing national security. The widespread applications of data mining technologies have raised concerns about trade secrecy of corporations and privacy of innocent people contained in the datasets collected and used for the data min...

We illustrate the use of the R-package rstiefel for matrix-variate data analysis in the context of two examples. The first example considers estimation of a reduced-rank mean matrix in the presence of normally distributed noise. The second example considers the modeling of a social network of friendships among teenagers. Bayesian estimation for these models requires the ability to simulate from...

Tracking and approximating data matrices in streaming fashion is a fundamental challenge. The problem requires more care and attention when data comes from multiple distributed sites, each receiving a stream of data. This paper considers the problem of “tracking approximations to a matrix” in the distributed streaming model. In this model, there are m distributed sites each observing a distinct...

1 Models 2 1.1 Matrix factorisation models . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Matrix factorisation with ARD and importance values . . . . . . . . . . . . . 8 1.3 Hybrid matrix factorisation model . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3.1 Model definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3.2 Gibbs sampler . . . . . . . . . ....

Matrix factorization is a popular approach to solving matrix estimation problems based on partial observations. Existing matrix factorization is based on least squares and aims to yield a low-rank matrix to interpret the conditional sample means given the observations. However, in many real applications with skewed and extreme data, least squares cannot explain their central tendency or tail di...

In this paper it is shown how to utilize Java arrays for matrix computations. We discuss the disadvantages of Java arrays when used as two-dimensional array for dense matrix computation, and how to improve the performance. We show how to create eÆcient dynamic data structure for sparse matrix computation using Java's native arrays. We construct a data structure for large sparse matrices that is...

A procedure is described for estimating evolutionary rate matrices from observed site frequency data. The procedure assumes (1) that the data are obtained from a constant size population evolving according to a stationary Wright-Fisher or decoupled Moran model; (2) that the data consist of a multiple alignment of a moderate number of sequenced genomes drawn randomly from the population; and (3)...

We give near-optimal distributions for the sparsification of large m n matrices, where m ! n, for example representing n observations over m attributes. Our algorithms can be applied when the non-zero entries are only available as a stream, i.e., in arbitrary order, and result in matrices which are not only sparse, but whose values are also highly compressible. In particular, algebraic operatio...

Abstract. This paper aims at improving non-negative matrix factorization (NMF) to facilitate data compression. An evolutionary updating strategy is proposed to solve the NMF problem iteratively based on three sets of updating rules including multiplicative, firefly and survival of the fittest rules. For data compression application, the quality of the factorized matrices can be evaluated by mea...

We describe our experiments with phrasebased machine translation for the WMT 2012 Shared Task. We trained one system for 14 translation directions between English or Czech on one side and English, Czech, German, Spanish or French on the other side. We describe a set of results with different training data sizes and subsets.