Partial least squares on graphical processor for efficient pattern recognition

Partial least squares (PLS) methods have recently been used for many patternrecognition 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.