Dimensionality Reduction A Global Geometric Framework for Nonlinear
نویسنده
چکیده
http://www.sciencemag.org/cgi/content/full/290/5500/2319 version of this article at: including high-resolution figures, can be found in the online Updated information and services, http://www.sciencemag.org/cgi/content/full/290/5500/2319/DC1 can be found at: Supporting Online Material found at: can be related to this article A list of selected additional articles on the Science Web sites http://www.sciencemag.org/cgi/content/full/290/5500/2319#related-content http://www.sciencemag.org/cgi/content/full/290/5500/2319#otherarticles , 11 of which can be accessed for free: cites 25 articles This article 357 article(s) on the ISI Web of Science. cited by This article has been http://www.sciencemag.org/cgi/content/full/290/5500/2319#otherarticles 27 articles hosted by HighWire Press; see: cited by This article has been http://www.sciencemag.org/cgi/collection/psychology Psychology : subject collections This article appears in the following
منابع مشابه
Nonlinear manifold learning for dynamic shape and dynamic appearance
Our objective is to learn representations for the shape and the appearance of moving (dynamic) objects that support tasks such as synthesis, pose recovery, reconstruction, and tracking. In this paper, we introduce a framework that aim to learn a landmark-free correspondence-free global representations of dynamic appearance manifolds. We use nonlinear dimensionality reduction to achieve an embed...
متن کاملMetric Learning on Manifolds
In recent years, manifold learning has become increasingly popular as a tool for performing nonlinear dimensionality reduction. This has led to the development of numerous algorithms of varying degrees of complexity that aim to recover manifold geometry using either local or global features of the data. Building on the Laplacian Eigenmap framework, we propose a new paradigm that offers a guaran...
متن کاملA global geometric framework for nonlinear dimensionality reduction.
Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain confronts the same problem in everyday perception, extracting from its high-dimension...
متن کاملDimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
We address instance-based learning from a perceptual organization standpoint and present methods for dimensionality estimation, manifold learning and function approximation. Under our approach, manifolds in high-dimensional spaces are inferred by estimating geometric relationships among the input instances. Unlike conventional manifold learning, we do not perform dimensionality reduction, but i...
متن کاملJoshua B . Tenenbaum Reduction A Global Geometric Framework for Nonlinear Dimensionality
clicking here. colleagues, clients, or customers by , you can order high-quality copies for your If you wish to distribute this article to others here. following the guidelines can be obtained by Permission to republish or repurpose articles or portions of articles ): September 20, 2013 www.sciencemag.org (this information is current as of The following resources related to this article are a...
متن کامل2D Dimensionality Reduction Methods without Loss
In this paper, several two-dimensional extensions of principal component analysis (PCA) and linear discriminant analysis (LDA) techniques has been applied in a lossless dimensionality reduction framework, for face recognition application. In this framework, the benefits of dimensionality reduction were used to improve the performance of its predictive model, which was a support vector machine (...
متن کامل