Vector Space Basis Change in Information Retrieval

Abstract

The Vector Space Basis Change (VSBC) is an algebraic operator responsible for change of basis and it is parameterized by a transition matrix. If we change the vector space basis, then each vector com ponent changes depending on this matrix. The strategy of VSBC has been shown to be effective in separating relevant documents and irrelevant ones. Recently, using this strategy, some feedback algorithms have been de veloped. To build a transition matrix some optimization methods have been used. In this paper, we propose to use a simple, convenient and direct method to build a transition matrix. Based on this method we develop a relevance feedback algorithm. Experimental results on a TREC collection show that our proposed method is effective and generally superior to known VSBC-based models. We also show that our proposed method gives a statistically significant improvement over these models.

Towards a Better Basis Search through a Surrogate Model-Based Epistasis Minimization for Pseudo-Boolean Optimization

“…When we use a basis other than the standard basis, the structure of problem space can be quite different from the original one. Mbarek et al [3,4] changed the standard basis of a vector space to ensure the efficient performance of an algorithm. Seo et al [5] modified problem space by nontrivial encoding through a method changing the standard basis.…”