Concept Indexing - A Fast Dimensionality Reduction Algorithm with Applications to Document Retrieval & Categorization

In recent years, we have seen a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intranets. This has led to an increased interest in developing methods that can efficiently categorize and retrieve relevant information. Retrieval techniques based on dimensionality reduction, such as Latent Semantic Indexing (LSI), have been shown to improve the quality of the information being retrieved by capturing the latent meaning of the words present in the documents. Unfortunately, the high computational requirements of LSI and its inability to compute an effective dimensionality reduction in a supervised setting limits its applicability. In this paper we present a fast dimensionality reduction algorithm, called concept indexing (CI) that is equally effective for unsupervised and supervised dimensionality reduction. CI computes a k-dimensional representation of a collection of documents by first clustering the do..