Latent semantic indexing is an optimal special case of multidimensional scaling

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Abstract
Latent Semantic Indexing (LSI) is a technique for representing documents, queries, and terms as vectors in a multidimensional real-valued space. The representtions are approximations to the original term space encoding, and are found using the matrix technique of Singular Value Decomposition. In comparison Multidimensional Scaling (MDS) is a class of data analysis techniques for representing data points as points in a multidimensional real-valued space. The objects are represented so that inter-point similarities in the space match inter-object similarity information provided by the researcher. We illustrate how the document representations given by LSI are equivalent to the optimal representations found when solving a particular MDS problem in which the given inter-object similarity information is provided by the inner product similarities between the documents themselves. We further analyze a more general MDS problem in which the interdocument similarity information, although still in inner product form is arbitrary with respect to the vector space encoding of the documents.

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