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Feature extraction is one of the most basic problems of data stream processing especially in big data era. However, when traditional feature extraction algorithms deal with the big data streams, they are unable to solve the existing big data stream's high-dimensional nonlinear problems. In this paper, we propose a new Semi-Supervised Local Preserving Embedding algorithm(SSLPE), which combines the...
Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationship of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral image (HSI) that possesses many homogenous areas. In this paper,...
Marginal Fisher Analysis(MFA) is a typical supervised subspace embedding method which has been used in dimensionality reduction. The projection matrixes are obtained by maximizing the intraclass compactness and simultaneously minimizing the intraclass separability. But in practical applications, no sufficient labeled training samples with prior knowledge was provided, so unlabeled image data are eager...
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