The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 06, 2013

Filed:

May. 25, 2010
Applicants:

BO Thiesson, Woodinville, WA (US);

Chong Wang, Princeton, NJ (US);

Inventors:

Bo Thiesson, Woodinville, WA (US);

Chong Wang, Princeton, NJ (US);

Assignee:

Microsoft Corporation, Redmond, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 15/18 (2006.01); G06K 9/62 (2006.01);
U.S. Cl.
CPC ...
G06K 9/6226 (2013.01);
Abstract

Described are variational Expectation Maximization (EM) embodiments for learning a mixture model using component-dependent data partitions, where the E-step is sub-linear in sample size while the algorithm still maintains provable convergence guarantees. Component-dependent data partitions into blocks of data items are constructed according to a hierarchical data structure comprised of nodes, where each node corresponds to one of the blocks and stores statistics computed from the data items in the corresponding block. A modified variational EM algorithm computes the mixture model from initial component-dependent data partitions and a variational R-step updates the partitions. This process is repeated until convergence. Component membership probabilities computed in the E-step are constrained such that all data items belonging to a particular block in a particular component-dependent partition behave in the same way. The E-step can therefore consider the blocks or chunks of data items via their representative statistics, rather than considering individual data items.


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