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Please use this identifier to cite or link to this item: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21330
Title: 流形學習回顧
A Review of Manifold Learning Algorithms
Authors: Yi-Ping Huang
黃毅平
Advisor: 王藹農(Ai-Nung Wang)
Keyword: 流形學習,
Manifold Learning,
Publication Year : 2019
Degree: 碩士
Abstract: 流形學習是降低資料維度的方法,可分為線性以及非線性的。非線性的方法有 Laplacian eigenmaps 和 locally linear embeddings 等。線性的方法有 MDS、ISOMAP、LPP 以及他們的衍生。這些方法的解可由跡數最小化問題得來,並等價於特徵值問題。我們給一個通用的架構並討論他們之間的關係。
Manifold learning algorithms are techniques utilized to reduce the dimen­ sion of data sets. These methods includes the nonlinear (implicit) ones, and the linear (projective) ones. Among the nonlinear are Laplacian eigenmaps and locally linear embeddings (LLE); and among the linear are metric multi­ dimensional scaling (MDS), ISOMAP, locally preserving projections (LPP) and derivatives of them. All these methods give rise to trace minimization problems and, as a result, eigenvalue problems. We give a common frame­ work for them and discuss their relationships.
URI: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21330
DOI: 10.6342/NTU201903104
Fulltext Rights: 未授權
Appears in Collections:數學系

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