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2001
ACM

On the Effects of Dimensionality Reduction on High Dimensional Similarity Search

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On the Effects of Dimensionality Reduction on High Dimensional Similarity Search
The dimensionality curse has profound e ects on the effectiveness of high-dimensional similarity indexing from the performance perspective. One of the well known techniques for improving the indexing performance is the method of dimensionality reduction. In this technique, the data is transformed to a lower dimensional space by nding a new axissystem in which most of the data variance is preserved in a few dimensions. This reduction may also have a positive effect on the quality of similarity for certain data domains such as text. For other domains, it may lead to loss of information and degradation of search quality. Recent research indicates that the improvement for the text domain is caused by the re-enforcement of the semantic concepts in the data. In this paper, we provide an intuitive model of the e ects of dimensionality reduction on arbitrary high dimensional problems. We provide an e ective diagnosis of the causality behind the qualitative e ects of dimensionality reduction o...
Charu C. Aggarwal
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2001
Where PODS
Authors Charu C. Aggarwal
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