Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Feature extraction using sequential semidefinite programming|
|Citation:||Proceedings of DICTA / pp.430-437|
|Conference Name:||Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (9th : 2007 : Glenelg, Australia)|
|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|
|Appears in Collections:||Computer Science publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.