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Poster
Dimensionality Reduction of Massive Sparse Datasets Using Coresets
Dan Feldman · Mikhail Volkov · Daniela Rus

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #89
in Tuesday Posters »
In this paper we present a practical solution with performance guarantees to the problem of dimensionality reduction for very large scale sparse matrices. We show applications of our approach to computing the Principle Component Analysis (PCA) of any $n\times d$ matrix, using one pass over the stream of its rows. Our solution uses coresets: a scaled subset of the $n$ rows that approximates their sum of squared distances to \emph{every} $k$-dimensional \emph{affine} subspace. An open theoretical problem has been to compute such a coreset that is independent of both $n$ and $d$. An open practical problem has been to compute a non-trivial approximation to the PCA of very large but sparse databases such as the Wikipedia document-term matrix in a reasonable time. We answer both of these questions affirmatively. Our main technical result is a new framework for deterministic coreset constructions based on a reduction to the problem of counting items in a stream.
Keywords: Clustering · Matrix Factorization · (Application) Information Retrieval · Sparsity and Feature Selection · Component Analysis (ICA,PCA,CCA, FLDA)
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Author Information

Dan Feldman (University of Haifa)
Mikhail Volkov (MIT)
Daniela Rus (MIT)

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