Generalized Principal Component Analysis (GPCA)
published: Feb. 25, 2007, recorded: January 2005, views: 19345
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Data segmentation is usually though of as a chicken-and-egg problem. In order to estimate a mixture of models one needs to first segment the data, and in order to segment the data one needs to know the model parameters. Therefore, data segmentation is usually solved in two stages
1. Data clustering and 2. Model fitting.
Other iterative methods use, e.g. the Expectation Maximization (EM) algorithm. This talk will show that for a wide class of segmentation problems with multi-linear structure (including clustering subspaces of unknown and varying dimensions), the chicken-and-egg dilemma can be tackled as follows:
1. Fit a set of polynomials to all data points, without clustering the data 2. Obtain the model parameters for each group from the derivatives of these polynomials.
Applications of GPCA to image/video/motion segmentation, face clustering, and identification of hybrid dynamical models systems will also be presented.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !