Partial least squares on graphical processor for efficient pattern recognition
Partial least squares on graphical processor for efficient pattern recognition
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Date
2010-10-18
Authors
Srinivasan, Balaji Vasan
Schwartz, William Robson
Duraiswami, Ramani
Davis, Larry
Advisor
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Abstract
Partial least squares (PLS) methods have recently been used for many pattern
recognition problems in computer vision. Here, PLS is primarily used as a
supervised dimensionality reduction tool to obtain effective feature
combinations for better learning. However, application of PLS to large datasets
is hindered by its higher computational cost. We propose an approach to
accelerate the classical PLS algorithm on graphical processors to obtain the
same performance at a reduced cost. Although, PLS modeling is practically an
offline training process, accelerating it helps large scale modeling. The
proposed acceleration is shown to perform well and it yields upto ~30X speedup,
It is applied on standard datasets in human detection and face recognition.