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Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension

Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension

This video was recorded at Machine Learning Summer School (MLSS), Taipei 2006. We design an on-line algorithm for Principal Component Analysis. The instances are projected into a probabilistically chosen low dimensional subspace. The total expected quadratic approximation error equals the total quadratic approximation error of the best subspace chosen in hindsight plus some additional term that grows linearly in dimension of the subspace but logarithmically in the dimension of the instances.
Keywords:
videolectures, ocwc, oec
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More about this material

Material Type:
Presentation
Date Added to MERLOT:
February 10, 2015
Date Modified in MERLOT:
February 10, 2015
Author:
Submitter:
The Open Education Consortium
Primary Audience:
College General Ed, College Lower Division, College Upper Division
Technical Format:
Video
Mobile Compatibility:
Not specified at this time
Language:
English
Cost Involved:
No
Source Code Available:
No
Creative Commons:
Creative Commons License
This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States

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