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Author Chen, Hwann-Tzong ♦ Chang, Huang-Wei ♦ Liu, Tyng-Luh
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Discriminating Nature ♦ New Approach ♦ Low-dimensional Subspace ♦ Optimization Problem ♦ Neighbor Rule ♦ Comprehensive Comparison ♦ Different Class ♦ Face Recognition ♦ Via Embedding ♦ Extensive Experiment ♦ Data Point ♦ Pattern Classification ♦ Classification Problem ♦ New Test Data ♦ Intrinsic Neighbor Relation ♦ Useful Variant ♦ Class Relation ♦ Local Discriminant Embedding ♦ Twodimensional Lde
Description We present a new approach, called local discriminant embedding (LDE), to manifold learning and pattern classification. In our framework, the neighbor and class relations of data are used to construct the embedding for classification problems. The proposed algorithm learns the embedding for the submanifold of each class by solving an optimization problem. After being embedded into a low-dimensional subspace, data points of the same class maintain their intrinsic neighbor relations, whereas neighboring points of different classes no longer stick to one another. Via embedding, new test data are thus more reliably classified by the nearest neighbor rule, owing to the locally discriminating nature. We also describe two useful variants: twodimensional LDE and kernel LDE. Comprehensive comparisons and extensive experiments on face recognition are included to demonstrate the effectiveness of our method. 1.
In Proc. IEEE Conf. Computer Vision and Pattern Recognition
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2005-01-01