منابع مشابه
Sequential nonlinear manifold learning
The computation of compact and meaningful representations of high dimensional sensor data has recently been addressed through the development of Nonlinear Dimensional Reduction (NLDR) algorithms. The numerical implementation of spectral NLDR techniques typically leads to a symmetric eigenvalue problem that is solved by traditional batch eigensolution algorithms. The application of such algorith...
متن کاملNonlinear Manifold Learning
The aim of this report is to study the two nonlinear manifold learning techniques recently proposed (Isomap [12] and Locally Linear Embedding (LLE) [8]) and suggest directions for further research. First the Isomap and the LLE algorithm are discussed in detail. Some of the areas that need further work are pointed out. A few novel applications which could use these two algorithms have been discu...
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Manifold learning is the process of estimating a low-dimensional structure which underlies a collection of high-dimensional data. Here we review two popular methods for nonlinear dimensionality reduction, locally linear embedding (LLE, [1]) and IsoMap [2]. We also discuss their roots in principal component analysis and multidimensional scaling, and provide a brief comparison of the underlying a...
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Dimensionality reduction is required to produce visualizations of high dimensional data. In this framework, one of the most straightforward approaches to visualising high dimensional data is based on reducing complexity and applying linear projections while tumbling the projection axes in a defined sequence which generates a Grand Tour of the data. We propose using smooth nonlinear topographic ...
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Manifold learning addresses the problem of finding low–dimensional structure within collections of high–dimensional data. Recent interest in this problem was motivated by the development of a pair of algorithms, locally linear embedding (LLE) [6] and isometric feature mapping (IsoMap) [8]. Both methods use local, linear relationships to derive global, nonlinear structure, although their specifi...
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ژورنال
عنوان ژورنال: Intelligent Data Analysis
سال: 2007
ISSN: 1571-4128,1088-467X
DOI: 10.3233/ida-2007-11207