Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/44936
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Type: Conference paper
Title: Feature extraction using sequential semidefinite programming
Author: Shen, C.
Li, H.
Brooks, M.
Citation: Proceedings of DICTA / pp.430-437
Publisher: IEEE
Publisher Place: CDROM
Issue Date: 2007
ISBN: 0769530672
9780769530673
Conference Name: Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (9th : 2007 : Glenelg, Australia)
Statement of
Responsibility: 
Shen, Chunhua, Li, Hongdong and Brooks, Michael J.
Abstract: Many feature extraction approaches end up with a trace quotient formulation. Since it is difficult to directly solve the trace quotient problem, conventionally the trace quotient cost is replaced by an approximation such that the generalised eigen-decomposition can be applied. In this work we directly optimise the trace quotient. It is reformulated as a quasi-linear semidefinite optimisation problem, which can be solved globally and efficiently using standard off-the-shelf semidefinite programming solvers. Also this optimisation strategy allows one to enforce additional constraints ( e.g., sparseness constraints) on the projection matrix. Based on this optimisation framework, a novel feature extraction algorithm is designed. Its advantages are demonstrated on several UCI machine learning benchmark datasets, USPS handwritten digits and ORL face data.
Rights: © Copyright 2008 IEEE – All Rights Reserved
RMID: 0020076181
DOI: 10.1109/DICTA.2007.4426829
Appears in Collections:Computer Science publications

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