Analyzing Magnification Factors and Principal Spread Directions in Manifold Learning
نویسندگان
چکیده
Great amount of data under varying intrinsic features is thought of as high dimensional nonlinear manifold in the observation space. How to analyze the mapping relationship between the high dimensional manifold and the corresponding intrinsic low dimensional one quantitatively is important to machine learning and cognitive science. In this paper, we propose SVD (singular value decomposition) based magnification factors and spread direction for quantitative analyzing the relationship. The result of conducting experiments on several databases show the advantages of this proposed SVD-based approach in manifold learning.
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