A visualization metric for dimensionality reduction
Data visualization of high-dimensional data is possible through the use of dimensionality reduction techniques. However, in deciding which dimensionality reduction techniques to use in practice, quantitative metrics are necessary for evaluating the results of the transformation and visualization of...
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sg-ntu-dr.10356-847872020-03-07T13:57:29Z A visualization metric for dimensionality reduction Tsai, Flora S. School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering Data visualization of high-dimensional data is possible through the use of dimensionality reduction techniques. However, in deciding which dimensionality reduction techniques to use in practice, quantitative metrics are necessary for evaluating the results of the transformation and visualization of the lower dimensional embedding. In this paper, we propose a manifold visualization metric based on the pairwise correlation of the geodesic distance in a data manifold. This metric is compared with other metrics based on the Euclidean distance, Mahalanobis distance, City Block metric, Minkowski metric, cosine distance, Chebychev distance, and Spearman distance. The results of applying different dimensionality reduction techniques on various types of nonlinear manifolds are compared and discussed. Our experiments show that our proposed metric is suitable for quantitatively evaluating the results of the dimensionality reduction techniques if the data lies on an open planar nonlinear manifold. This has practical significance in the implementation of knowledge-based visualization systems and the application of knowledge-based dimensionality reduction methods. 2013-07-10T06:28:43Z 2019-12-06T15:51:09Z 2013-07-10T06:28:43Z 2019-12-06T15:51:09Z 2011 2011 Journal Article https://hdl.handle.net/10356/84787 http://hdl.handle.net/10220/11109 10.1016/j.eswa.2011.08.080 en Expert systems with applications © 2011 Elsevier Ltd. |
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DRNTU::Engineering::Computer science and engineering Tsai, Flora S. A visualization metric for dimensionality reduction |
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Data visualization of high-dimensional data is possible through the use of dimensionality reduction techniques. However, in deciding which dimensionality reduction techniques to use in practice, quantitative metrics are necessary for evaluating the results of the transformation and visualization of the lower dimensional embedding. In this paper, we propose a manifold visualization metric based on the pairwise correlation of the geodesic distance in a data manifold. This metric is compared with other metrics based on the Euclidean distance, Mahalanobis distance, City Block metric, Minkowski metric, cosine distance, Chebychev distance, and Spearman distance. The results of applying different dimensionality reduction techniques on various types of nonlinear manifolds are compared and discussed. Our experiments show that our proposed metric is suitable for quantitatively evaluating the results of the dimensionality reduction techniques if the data lies on an open planar nonlinear manifold. This has practical significance in the implementation of knowledge-based visualization systems and the application of knowledge-based dimensionality reduction methods. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Tsai, Flora S. |
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Tsai, Flora S. |
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Tsai, Flora S. |
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A visualization metric for dimensionality reduction |
title_short |
A visualization metric for dimensionality reduction |
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A visualization metric for dimensionality reduction |
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A visualization metric for dimensionality reduction |
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A visualization metric for dimensionality reduction |
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visualization metric for dimensionality reduction |
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2013 |
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https://hdl.handle.net/10356/84787 http://hdl.handle.net/10220/11109 |
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