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| A Weighted Semi-Supervised Dimensionality Reduction Method Based Locally Linear Graph |
| Tianjun Lu, Shenglan Liu, Huibing Wang, Le Wang |
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Abstract In recent years, dimensionality reduction has been widely used in
image processing, pattern recognition, and other related fields, and
achieved a good performance. Especially supervised dimensionality
reduction algorithms have been successfully applied to preprocess of
data in classification. However, in many practical applications, the
size of labeled sample is generally too small for traditional
supervised\linebreak dimensionality reduction algorithms (such as
Linear Discriminant Analysis, LDA) to overcome the Small Sample Size
(SSS) problem; moreover, a small amount of labeled samples usually
lead the projected data into an inappropriate low-dimensional
subspace. In order to solve the two problems above, this paper
proposes a Weighted Semi-supervised Maximum Margin Criterion (WSMMC)
algorithm. Our method utilizes the Locally Linear Embedding Graph to
characterize the spatial distribution of non-labeled samples and
avoid the SSS problem by taking advantage of MMC. Experimental
results show that the algorithm is effective.
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| DOI:10.12733/jics20104385 |
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