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Recent Advances in Supervised Dimension Reduction: A Survey

1
School of Information Systems, Singapore Management University, Singapore 178902, Singapore
2
Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
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School of Computer Science and Technology, Nantong University, Nantong 226019, China
*
Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2019, 1(1), 341-358; https://doi.org/10.3390/make1010020
Received: 28 October 2018 / Revised: 28 December 2018 / Accepted: 28 December 2018 / Published: 7 January 2019
(This article belongs to the Section Learning)
Recently, we have witnessed an explosive growth in both the quantity and dimension of data generated, which aggravates the high dimensionality challenge in tasks such as predictive modeling and decision support. Up to now, a large amount of unsupervised dimension reduction methods have been proposed and studied. However, there is no specific review focusing on the supervised dimension reduction problem. Most studies performed classification or regression after unsupervised dimension reduction methods. However, we recognize the following advantages if learning the low-dimensional representation and the classification/regression model simultaneously: high accuracy and effective representation. Considering classification or regression as being the main goal of dimension reduction, the purpose of this paper is to summarize and organize the current developments in the field into three main classes: PCA-based, Non-negative Matrix Factorization (NMF)-based, and manifold-based supervised dimension reduction methods, as well as provide elaborated discussions on their advantages and disadvantages. Moreover, we outline a dozen open problems that can be further explored to advance the development of this topic. View Full-Text
Keywords: supervised learning; dimension reduction; representation learning; principal component analysis; nonnegative matrix factorization; manifold learning supervised learning; dimension reduction; representation learning; principal component analysis; nonnegative matrix factorization; manifold learning
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Chao, G.; Luo, Y.; Ding, W. Recent Advances in Supervised Dimension Reduction: A Survey. Mach. Learn. Knowl. Extr. 2019, 1, 341-358.

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