Abstract
Sparse coding, which produces a vector representation based on sparse linear combination of dictionary atoms, has been widely applied in signal processing, data mining and neuroscience. Constructing a proper dictionary for sparse coding is a common challenging problem. In this paper, we treat dictionary learning as an unsupervised learning process, and propose a Laplacian score dictionary (LSD). This new learning method uses local geometry information to select atoms for the dictionary. Comparisons with alternative clustering based dictionary learning methods are conducted. We also compare LSD with full-training-data-dictionary and others classic methods in the experiments. The classification performances on binary-class datasets and multi-class datasets from UCI repository demonstrate the effectiveness and efficiency of our method.
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Xu, J., Man, H. (2011). Dictionary Learning Based on Laplacian Score in Sparse Coding. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_19
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DOI: https://doi.org/10.1007/978-3-642-23199-5_19
Publisher Name: Springer, Berlin, Heidelberg
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