Semi-supervised classification based on subspace sparse representation
Data(s) |
01/04/2015
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Resumo |
Graph plays an important role in graph-based semi-supervised classification. However, due to noisy and redundant features in high-dimensional data, it is not a trivial job to construct a well-structured graph on high-dimensional samples. In this paper, we take advantage of sparse representation in random subspaces for graph construction and propose a method called Semi-Supervised Classification based on Subspace Sparse Representation, SSC-SSR in short. SSC-SSR first generates several random subspaces from the original space and then seeks sparse representation coefficients in these subspaces. Next, it trains semi-supervised linear classifiers on graphs that are constructed by these coefficients. Finally, it combines these classifiers into an ensemble classifier by minimizing a linear regression problem. Unlike traditional graph-based semi-supervised classification methods, the graphs of SSC-SSR are data-driven instead of man-made in advance. Empirical study on face images classification tasks demonstrates that SSC-SSR not only has superior recognition performance with respect to competitive methods, but also has wide ranges of effective input parameters. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Springer |
Relação |
http://dro.deakin.edu.au/eserv/DU:30060823/yu-semisupervised-inpress-2013.pdf http://dro.deakin.edu.au/eserv/DU:30060823/zhang-semisupervised-2015.pdf http://doi.org/10.1007/s10115-013-0702-2 |
Direitos |
2013, Springer |
Palavras-Chave | #Graph construction #High-dimensional data #Semi-supervised classification #Subspaces sparse representation |
Tipo |
Journal Article |