Subspace learning techniques are widespread in pattern recognition research. They include Principal Component Analysis (PCA), Locality Preserving Projection (LPP), etc. These tech...
This paper presents a novel classification/ retrieval system for motion events based on a perfect view invariant representation of motion trajectories and a linear classifier al...
Eser Ustunel, Xu Chen, Dan Schonfeld, Ashfaq A. Kh...
Abstract. We apply independent component analysis (ICA) for learning an efficient color image representation of natural scenes. In the spectra of single pixels, the algorithm was a...
Te-Won Lee, Thomas Wachtler, Terrence J. Sejnowski
Abstract—We present an algorithm for removing timefrequency components, found by a standard Gabor transform, of a “real-world” sound while causing no audible difference to th...
Low-dimensional representations of sensory signals are key to solving many of the computational problems encountered in high-level vision. Principal Component Analysis (PCA) has b...