Results 231 to 240 of about 115,342 (279)

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 Barlow   +3 more
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Nonlinear Dimensionality Reduction by Locally Linear Inlaying

IEEE Transactions on Neural Networks, 2009
High-dimensional data is involved in many fields of information processing. However, sometimes, the intrinsic structures of these data can be described by a few degrees of freedom. To discover these degrees of freedom or the low-dimensional nonlinear manifold underlying a high-dimensional space, many manifold learning algorithms have been proposed ...
Yuexian, Hou, Peng, Zhang, Xingxing, Xu, Xiaowei, Zhang, Wenjie, Li   +4 more
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Incremental nonlinear dimensionality reduction by manifold learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
Understanding the structure of multidimensional patterns, especially in unsupervised cases, is of fundamental importance in data mining, pattern recognition, and machine learning. Several algorithms have been proposed to analyze the structure of high-dimensional data based on the notion of manifold learning.
Martin H C, Law, Anil K, Jain
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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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Dimensional Reduction for Nonlinear Boundary Value Problems

SIAM Journal on Numerical Analysis, 1988
The paper describes a dimension reduction method for a class of strongly nonlinear boundary value problems. The idea is to choose a priori a basis of functions for one of the variables by ways of asymptotic expansions. Numerical experiments are presented.
Jensen, Soren, Babuška, Ivo
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Locally Adaptive Nonlinear Dimensionality Reduction

2006
Popular nonlinear dimensionality reduction algorithms, e.g., SIE and Isomap suffer a difficulty in common: global neighborhood parameters often fail in tackling data sets with high variation in local manifold. To improve the availability of nonlinear dimensionality reduction algorithms in the field of machine learning, an adaptive neighbors selection ...
Yuexian Hou, Hongmin Yang, Pilian He
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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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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 A. Lee
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DISCRIMINATIVE NONLINEAR DIMENSIONALITY REDUCTION FOR IMPROVED CLASSIFICATION

International Journal of Neural Systems, 1994
Multi-Layer Perceptron (MLP) neural networks have been used extensively for classification tasks. Typically, the MLP network is trained explicitly to produce the correct classification as its output. For speech recognition, however, several investigators have recently experimented with an indirect approach: a unique MLP predictive network is trained ...
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Supervised Dimensionality Reduction via Nonlinear Target Estimation

2013
Dimensionality reduction is a crucial ingredient of machine learning and data mining, boosting classification accuracy through the isolation of patterns via omission of noise. Nevertheless, recent studies have shown that dimensionality reduction can benefit from label information, via a joint estimation of predictors and target variables from a low ...
Grabocka J., Drumond L., Schmidt-Thieme L.   +2 more
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