Results 11 to 20 of about 4,379,261 (288)
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 +3 more
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Nonlinear dimensionality reduction in climate data [PDF]
Nonlinear Processes in Geophysics, 2004 Linear methods of dimensionality reduction are useful tools for handling and interpreting high dimensional data. However, the cumulative variance explained by each of the subspaces in which the data space is decomposed may show a slow convergence that ...
A. J. Gámez +3 more
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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 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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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.
Tenenbaum, J. B. +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 +5 more
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A biological model of nonlinear dimensionality reduction. [PDF]
Sci Adv, 2023 Abstract Obtaining appropriate low-dimensional representations from high-dimensional sensory inputs in an unsupervised manner is essential for straightforward downstream processing. Although nonlinear dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE) have been developed, their implementation in
Yoshida K, Toyoizumi T.
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Nonlinear dimensionality reduction for clustering [PDF]
Pattern Recognition, 2020 Abstract We introduce an approach to divisive hierarchical clustering that is capable of identifying clusters in nonlinear manifolds. This approach uses the isometric mapping (Isomap) to recursively embed (subsets of) the data in one dimension, and then performs a binary partition designed to avoid the splitting of clusters.
Sotiris K. Tasoulis +2 more
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Nonlinear dimensionality reduction on graphs [PDF]
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017 In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated from social, commercial and brain network interactions. Their efficient processing calls for dimensionality reduction techniques capable of properly compressing the data while
Yanning Shen +2 more
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Temporal nonlinear dimensionality reduction [PDF]
The 2011 International Joint Conference on Neural Networks, 2011 Existing Nonlinear dimensionality reduction (NLDR) algorithms make the assumption that distances between observations are uniformly scaled. Unfortunately, with many interesting systems, this assumption does not hold. We present a new technique called Temporal NLDR (TNLDR), which is specifically designed for analyzing the high-dimensional observations ...
Michael Gashler, Tony R. Martinez
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