Yinsong Pan

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Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for Hyperspectral Image Classification. The manifold learning methods are popular for dimensionality reduction, such as Locally Linear Embedding, Isomap, and Laplacian Eigenmap. However, a disadvantage of many manifold(More)
The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification. In this paper, we proposed a novel method, called semi-supervised marginal Fisher analysis (SSMFA), to(More)
Reducing the dimensionality of data without losing intrinsic information is an important preprocessing step in image recognition. Local Fisher Discriminant Analysis (LFDA) is a linear projective map that arises by solving the multimodal problem, which effectively combines the ideas of FDA and LPP. However, since the limited data pairs are employed to(More)
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