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| As a technology derived from the Human Visual System, sparse coding has attracted a lot of attentions in recent years. It aims to learn sparse coordinates in terms of the basis set, which is given directly or learned from the original data set. Because of the sparsity, the learned sparse representation can be used in further data processing( such as clustering and classifying) efficiently. But the canonical sparse coding methods are all ignored the intrinsic structure of the data. From the perspective of manifold learning, this paper propose a novel sparse coding method, called Sparse Coding based on Manifold learning (MSC). Inspired by LPP, MSC finds a basis set which can be used to represent the intrinsic manifold space of the data set, and then sparse representations will be learned in this space. The most obvious advantage of MSC compared with the algorithms which impose a manifold regularizer to the objective function directly is that MSC is nonparametric. In other words, MSC is more robust. A set of evaluations on real world applications demonstrate the effectiveness of this novel algorithm. |
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| Keywords:Pattern recognition, Cluster Analysis, Sparse coding, Manifold learning. |
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