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- W2383781046 abstract "As a new unsupervised learning technique, manifold learning has captured the attention of many researchers in the field of machine learning and cognitive sciences. The major algorithms include Isometric mapping (ISOMAP) and Locally Linear Embedding (LLE). The approaches can be used for discovering the intrinsic dimensions of nonlinear high-dimensional data effectively and aim researchers to analyze the data better. How to quantitatively analyze the relationship between the intrinsic dimensions and the observation space, however, has fewer reports. And thus further works in manifold learning may have suffered some difficulties. The paper focuses on two kinds of manifold learning algorithms (ISOMAP, LLE), and discusses magnification factors and principal spread directions from the observation space to the intrinsic low-dimensional space. Also the corresponding algorithm is proposed. Experiments show the effectiveness and advantages of the research." @default.
- W2383781046 created "2016-06-24" @default.
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- W2383781046 date "2005-01-01" @default.
- W2383781046 modified "2023-09-25" @default.
- W2383781046 title "Investigating Manifold Learning Algorithms Based on Magnification Factors and Principal Spread Directions" @default.
- W2383781046 hasPublicationYear "2005" @default.
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