This paper presents an alternative algorithm based on the singular value decomposition (SVD) that creates vector representation for linguistic units with reduced dimensionality. T...
The Singular Value Decomposition is a key operation in many machine learning methods. Its computational cost, however, makes it unscalable and impractical for applications involvi...
Michael P. Holmes, Alexander G. Gray, Charles Lee ...
The diffusion process on weblogs has attracted great interest since the early days of weblog studies. We propose a ranking technique which extracts topics and innovators by analyz...
Tadanobu Furukawa, Yutaka Matsuo, Ikki Ohmukai, Ko...
Following (Blitzer et al., 2006), we present an application of structural correspondence learning to non-projective dependency parsing (McDonald et al., 2005). To induce the corre...
Latent Semantic Analysis (LSA) is based on the Singular Value Decomposition (SVD) of a term-by-document matrix for identifying relationships among terms and documents from cooccur...
Static analysis tools tend to generate more alerts than a development team can reasonably examine without some form of guidance. In this paper, we propose a technique for leveragi...
Mark Sherriff, Sarah Smith Heckman, Mike Lake, Lau...
1 The latent semantic indexing (LSI) methodology for information retrieval applies the singular value decomposition to identify an eigensystem for a large matrix, in which cells re...
This paper introduces singular value decomposition (SVD) algorithms for some standard polynomial computations, in the case where the coefficients are inexact or imperfectly known....
Robert M. Corless, Patrizia M. Gianni, Barry M. Tr...
The blogosphere--the totality of blog-related Web sites-has become a great source of trend analysis in areas such as product survey, customer relationship, and marketing. Existing...
Abstract. A new algorithm for the incremental learning and non-intrusive tracking of the appearance of a previously non-seen face is presented. The computation is done in a causal ...