Speaker: Youness Aliyari, Toronto Rehabilitation Institute
Title: Mean shift type algorithms and their applications
Mean shift (MS) and subspace constrained mean shift (SCMS) algorithms are non-parametric, iterative methods to find a representation of a high dimensional data set on a principal curve or surface embedded in a high dimensional space. The representation of high dimensional data on a principal curve or surface, the class of mean shift type algorithms and their properties, and applications of these algorithms are the main focus of this talk.
I will give a brief review on principal curves and different algorithms to estimate them. Then, I will review the SCMS algorithm and its theoretical properties, as a recently proposed technique to find principal curves/surfaces. Finally, I will present new potential applications of the MS and SCMS algorithm. These applications involve finding straight lines in digital images; pre-processing data before applying locally linear embedding (LLE) and ISOMAP for dimensionality reduction; noisy source vector quantization where the clean data need to be estimated before the quanization step; improving the performance of kernel regression in certain situations; and skeletonization of digitally stored handwritten characters.