Adaptive label-driven scaling for latent semantic indexing

Authors:
Xiaojun Quan;Enhong Chen;Qiming Luo;Hui Xiong
Affiliations:
University of Science and Technology of China, Hefei, China;University of Science and Technology of China, Hefei, China;University of Science and Technology of China, Hefei, China;Rutgers University, Piscataway, NJ, USA
Venue:
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2008

Citing 3
Cited 0

A similarity-based probability model for latent semantic indexing

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Model-averaged latent semantic indexing

SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Supervised latent semantic indexing using adaptive sprinkling

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper targets on enhancing Latent Semantic Indexing (LSI) by exploiting category labels. Specifically, in the term-document matrix, the vector for each term either appearing in labels or semantically close to labels is scaled before performing Singular Value Decomposition (SVD) to boost its impact on the generated left singular vectors. As a result, the similarities among documents in the same category are increased. Furthermore, an adaptive scaling strategy is designed to better utilize the hierarchical structure of categories. Experimental results show that the proposed approach is able to significantly improve the performance of hierarchical text categorization.