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Published in:
2015 | OriginalPaper | Chapter
Locally Linear Representation Manifolds Margin
Authors : Bo Li, Yun-Qing Wang, Lei Lei, Zhang-Tao Fan
Published in: Intelligent Computing Theories and Methodologies
Publisher: Springer International Publishing
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Abstract
In this paper, a novel supervised multiple manifolds learning method is presented for dimensionality reduction, which is titled locally linear representation manifold margin (LLRMM). In the proposed LLRMM, both an inter-manifold graph and intra-manifold graph are constructed, where any point in the inter-manifold graph must select neighbors from other manifolds while the neighborhood in the intra-manifold graph are composed of samples from the same manifold. Then the least locally linear representation technique is introduced to optimize the reconstruction weights as well as the corresponding inter-manifold scatter and intra-manifold scatter, based on which manifolds margin can be reasoned. At last, a discriminant subspace is explored. Experiments on some benchmark face data sets have been conducted and experimental results show that the proposed method outperforms some related state-of-the-art dimensionality reduction methods.