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Semi-supervised subspace learning with L2graph
2016
Neurocomputing
Subspace learning aims to learn a projection matrix from a given training set so that a transformation of raw data to a low-dimensional representation can be obtained. In practice, the labels of some training samples are available, which can be used to improve the discrimination of low-dimensional representation. In this paper, we propose a semi-supervised learning method which is inspired by the biological observation of similar inputs having similar codes (SISC), i.e., the same collection of
doi:10.1016/j.neucom.2015.11.112
fatcat:ndxrci6a5bhn7jzjv4pjdcy7ma