Results 251 to 260 of about 26,484 (294)

Semi-supervised nonlinear dimensionality reduction

Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006
The problem of nonlinear dimensionality reduction is considered. We focus on problems where prior information is available, namely, semi-supervised dimensionality reduction. It is shown that basic nonlinear dimensionality reduction algorithms, such as Locally Linear Embedding (LLE), Isometric feature mapping (ISOMAP), and Local Tangent Space Alignment (
Xin Yang, Haoying Fu, Hongyuan Zha, Jesse L. Barlow   +3 more
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Nonlinear Dimensionality Reduction for Visualization

, 2013
The visual interpretation of data is an essential step to guide any further processing or decision making. Dimensionality reduction (or manifold learning) tools may be used for visualization if the resulting dimension is constrained to be 2 or 3. The field of machine learning has developed numerous nonlinear dimensionality reduction tools in the last ...
Michel Verleysen, John Aldo Lee
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Data visualization by nonlinear dimensionality reduction

WIREs Data Mining and Knowledge Discovery, 2015
In this overview, commonly used dimensionality reduction techniques for data visualization and their properties are reviewed. Thereby, the focus lies on an intuitive understanding of the underlying mathematical principles rather than detailed algorithmic pipelines. Important mathematical properties of the technologies are summarized in the tabular form.
Gisbrecht, Andrej, Hammer, Barbara
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Nonlinear Dimensionality Reduction by Locally Linear Embedding

Science, 2000
Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data.
Roweis, S. T., Lawrence, L. K.
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A Global Geometric Framework for Nonlinear Dimensionality Reduction

Science, 2000
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
Tenenbaum, J. B., de Silva, V., Langford, J. C.   +2 more
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Using Nonlinear Dimensionality Reduction to Visualize Classifiers

, 2013
Nonlinear dimensionality reduction (DR) techniques offer the possibility to visually inspect a given finite high-dimensional data set in two dimensions. In this contribution, we address the problem to visualize a trained classifier on top of these projections. We investigate the suitability of popular DR techniques for this purpose and we point out the
Schulz, Alexander, Gisbrecht, Andrej, Hammer, Barbara, Rojas, Ignacio, Joya, Gonzalo, Gabestany, Joan   +5 more
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Nonlinear barycentric dimensionality reduction

2010 IEEE International Conference on Image Processing, 2010
Many high-dimensional datasets can be mapped onto lower-dimensional linear simplexes, parametrized by barycentric coordinates. We present an unsupervised algorithm that is able to find the barycentric coordinates and corresponding vertices of such a high-dimensional dataset, by combining manifold learning with a distance geometry based algorithm for ...
Rob Heylen, Paul Scheunders
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Nonlinear Dimensionality Reduction

, 2007
Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. This book describes the methods to reduce the dimensionality of numerical databases.
Lee, John A, Verleysen, Michel, Lee, John Aldo   +2 more
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Nonlinear methods for clustering and reduction of dimensionality

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003
Analysis of data in computational finance and computational neuroscience share a number of common traits: data are typically massive, noisy, very high dimensional, and governed by complete multi-scale time dynamics. The set of known parameters forms a small subset of the true variates that control the dynamics of the systems from which data is ...
Hamid Eghbalnia, Amir H. Assadi, John D. Carew   +2 more
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Nonlinear dimensionality reduction by curvature minimization

2016 23rd International Conference on Pattern Recognition (ICPR), 2016
In this paper, we introduce a nonlinear dimensionality reduction (NLDR) technique that can construct a low-dimensional embedding efficiently and accurately with low embedding distortions. The key idea is to divide NLDR into nonlinearity reduction and linear dimensionality reduction, which simplifies the overall NLDR process.
Yusuke Yoshiyasu, Eiichi Yoshida
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