Abstract
Sparse representation is a parsimonious principle that a signal can be approximated by a sparse superposition of basis functions. The main topic of my thesis research is to apply this principle in the machine learning fields including classification, feature extraction, feature selection, and optimization.
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Li, Y. (2013). Sparse Representation for Machine Learning. In: Zaïane, O.R., Zilles, S. (eds) Advances in Artificial Intelligence. Canadian AI 2013. Lecture Notes in Computer Science(), vol 7884. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38457-8_38
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DOI: https://doi.org/10.1007/978-3-642-38457-8_38
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