Using latent semantic analysis to improve access to textual information
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mixtures of probabilistic principal component analyzers
Neural Computation
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Rank-preserving two-level caching for scalable search engines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Predictive caching and prefetching of query results in search engines
WWW '03 Proceedings of the 12th international conference on World Wide Web
SVDPACKC (Version 1.0) User''s Guide
SVDPACKC (Version 1.0) User''s Guide
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
Generalized low rank approximations of matrices
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Efficiency-quality tradeoffs for vector score aggregation
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
| Hi-index | 0.00 |
Latent Semantic Indexing is a classical method to produce optimal low-rank approximations of a term-document matrix. However, in the context of a particular query distribution, the approximation thus produced need not be optimal. We propose VLSI, a new query-dependent (or "variable") low-rank approximation that minimizes approximation error for any specified query distribution. With this tool, it is possible to tailor the LSI technique to particular settings, often resulting in vastly improved approximations at much lower dimensionality. We validate this method via a series of experiments on classical corpora, showing that VLSI typically performs similarly to LSI with an order of magnitude fewer dimensions.