Eecient Singular Value Decomposition via Improved Document Sampling Eecient Singular Value Decomposition via Improved Document Sampling
نویسندگان
چکیده
Singular value decomposition (SVD) is a general-purpose mathematical analysis tool that has been used in a variety of information-retrieval applications. As the size and complexity of retrieval collections increase, it is crucial for our analysis tools to scale accordingly. To this end, we have studied the application of a new theoretically justiied SVD approximation algorithm to the problem of text retrieval. We show that, in the case of latent semantic indexing, we can achieve near optimal approximations of the exact SVD using considerably less computation by using an appropriate distribution to sample the documents we include in our SVD analysis.
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