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* Research

Ph.D. Theses

Column Subset Selection for Approximating Matrices: Theory and Data Applications

By Ali Civril
Advisor: Malik Magdon-Ismail
December 2, 2009

In this thesis, we study the problem of selecting a subset of columns of a matrix so that they capture the important information contained in the matrix. We present complexity results and algorithms. The problem, in a very broad sense, asks for a "good" subset of columns of a given real matrix that provides a performance guarantee in terms of an objective function related to the spectrum of the matrix.

We first present a linear-time spectral graph drawing algorithm as a motivation which is a vast improvement over the standard quadratic-time method Classical Multidimensional Scaling (CMDS). To guarantee a fast implementation of the algorithm, it is desirable to quickly select a subset of columns of a distance matrix associated with the graph. Intuitively, in order to obtain a well conditioned sub-matrix, one has to choose a subset of column vectors -in a geometrical sense- such that they are as "far away" from each other as possible. We consider formalizations of this notion by studying the problem of selecting a subset of columns of size $k$ such that it satisfies some certain orthogonality conditions. We establish the NP-hardness of a few such problems and further show that they do not admit PTAS. For the problem of choosing the maximum volume sub-matrix, which we call MAX-VOL, we analyze a greedy algorithm and show that it provides a $2^{-O(k\log{k})}$ approximation. Our analysis of the greedy heuristic is tight to within a logarithmic factor in the exponent, which we show by explicitly constructing an instance for which the greedy heuristic is $2^{-\Omega(k)}$ from optimal. Further, we show that no efficient algorithm can appreciably improve upon the greedy algorithm by proving that MAX-VOL is NP-hard to approximate within $2^{-ck}$ for some constant $c$. Our proof is via a reduction from the Label-Cover problem.

Our last result is a constructive solution to the low-rank matrix approximation problem which asks for a subset of columns of a matrix that captures "most" of its spectrum. Our main result is a simple greedy deterministic algorithm with guarantees on the performance while choosing a small number of columns. Specifically, our greedy algorithm chooses $c$ columns from $A$ with $c=\tilde{O}\left(\frac{k^2\log k}{\epsilon^2} \mu^2(A)\right)$ such that

$${\|A-CC^+A\|}_F \leq \left(1+\epsilon \right)\norm{A-A_k}_F,$$

where $C$ is the matrix composed of the $c$ columns, $C^+$ is the pseudo- inverse of $C$ ($CC^+A$ is the best reconstruction of $A$ from $C$), and $\mu(A)$ is a measure of the \emph{coherence} in the normalized columns of $A$. To the best of our knowledge, this is the first deterministic algorithm with performance guarantees on the number of columns and a ($1+\epsilon)$ approximation ratio in Frobenius norm. Numerical results suggest that the performance of the algorithm might be far better than the theoretical bounds suggest.

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