Generalized Principal Component Analysis (GPCA)

author: René Vidal, Department of Biomedical Engineering, John Hopkins University
published: Feb. 25, 2007,   recorded: January 2005,   views: 19351


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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.

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Reviews and comments:

Comment1 NG, February 11, 2008 at 1:57 a.m.:

Part 1 is broken: video stops at the end of the 3rd minute.

Comment2 john, April 2, 2008 at 11:52 a.m.:

yes, i have the same problem with part 1

Comment3 B H SHEKAR, December 27, 2008 at 6:21 a.m.:

How to download these video lectures

Comment4 Vikram T N, January 28, 2009 at 4:51 p.m.:

Install orbit downloader to capture this streming video.

Comment5 ali, June 11, 2010 at 3:11 a.m.:

Dear Vikram, I have downloaded orbit, but I could not download the video. Would you please explain about it?

Comment6 usman, June 30, 2010 at 7:35 a.m.:

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Comment7 sajjad, August 1, 2010 at 3:42 a.m.:

dear manger
hi , very good site and score is 100 of 20 !!!
thank ,please avalible links of Video for downloadind

Comment8 stan, December 30, 2011 at 12:46 a.m.:

very good lecture, but why not attached any slide in Part 2?

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