منابع مشابه
Solving the Small Sample Size Problem of LDA
The small sample size problem is often encountered in pattern recognition. It results in the singularity of the within-class scatter matrix Sw in Linear Discriminant Analysis (LDA). Different methods have been proposed to solve this problem in face recognition literature. Some methods reduce the dimension of the original sample space and hence unavoidably remove the null space of Sw, which has ...
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Locality Preserving Projections (LPP) is a widely used manifold reduced dimensionality technique. However, it suffers from two problems: (1) Small Sample Size problem; (2)the performance is sensitive to the neighborhood size k. In order to address these problems, we propose an Exponential Locality Preserving Projections (ELPP) by introducing the matrix exponential in this paper. ELPP avoids the...
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The generalization of linear classifiers is considered for training sample sizes smaller than the feature size. It is shown that there exists a good linear classifier, that is better than the Nearest Mean classifier for sample sizes for which Fisher’s linear discriminant cannot be used. The use and performance of this small sample size classifier is illustrated by some examples.
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ژورنال
عنوان ژورنال: Nature Reviews Neuroscience
سال: 2013
ISSN: 1471-003X,1471-0048
DOI: 10.1038/nrn3475-c3