Latent semantic indexing is an optimal special case of multidimensional scaling
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
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A similarity-based probability model for latent semantic indexing
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Latent semantic indexing: a probabilistic analysis
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The document representation problem: an analysis of lsi and iterative residual rescaling
The document representation problem: an analysis of lsi and iterative residual rescaling
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A probabilistic model for Latent Semantic Indexing: Research Articles
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Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Term norm distribution and its effects on latent semantic indexing
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The uncovering of hidden structures by Latent Semantic Analysis
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Augmenting the power of LSI in text retrieval: Singular value rescaling
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Pattern Recognition Letters
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We consider the problem of creating document representations in which inter-document similarity measurements correspond to semantic similarity. We first present a novelsubspace-basedframework for formalizing this task. Using this framework, we derive a new analysis ofLatent Semantic Indexing(LSI), showing a precise relationship between its performance and theuniformityof the underlying distribution of documents over topics. This analysis helps explain the improvements gained by Ando's (2000)Iterative Residual Rescaling(\ours) algorithm: \ours\ can compensate for distributional non-uniformity. A further benefit of our framework is that it provides a well-motivated, effective method for automatically determining the rescaling factor \ours\ depends on, leading to further improvements. A series of experiments over various settings and with several evaluation metrics validates our claims.