Iterative residual rescaling
Rie Kubota Ando, Lillian Lee · 2001
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.