Computer Engineering Dept. |
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SEMINAR
Column Subset Selection Problem in Matrices: Ali Çivril Computer Science Dept, Rensselaer Polytechnic Institute, Troy,
NY, USA June 19, 2008, Fri @ 13:40 |
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We
are interested, in a broad sense, the problem of selecting a subset of
columns of a given matrix A so as
to capture its whole spectrum as much as possible. This notion, quantified in
a few different ways have long been tackled by the numerical linear algebra
and theoretical computer science community in the past two decades. All
related to this general scheme, our talk has three parts: 1) We first present
linear time spectral graph drawing algorithm SSDE, based on an approximate
decomposition of a distance matrix associated with the graph. Our algorithm
is a vast improvement over the well known method Classical Multi-Dimensional
Scaling (CMDS) and prevents from computing the whole distance matrix. 2)
Motivated by the practical concerns of SSDE, we define the problem MAX-VOL,
in which we seek a subset of columns of a matrix, whose parallelepiped has
the maximum volume over all possible choices. Together with a few other
related problems, we establish its NP-hardness and show that it does not
admit PTAS. We also present an approximation algorithm for MAX-VOL along with
a lower bound for the algorithm. 3) We generalize the sparse approximation
problem and present an algorithm for choosing a subset of columns of a matrix
A, to approximate a predefined
subspace. In case this subspace is the best rank-k approximation to A,
we have a relative error low-rank matrix approximation algorithm. This is the
first deterministic algorithm which has provably (1+ |
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Ali Civril obtained his BS degree from
Bilkent University in Ankara in 2004, and MS degree from Rensselaer
Polytechnic Institute (NY, USA) in 2007. He is still continuing his PhD
studies at the same institute. |