Journal Article10.1109/msp.2010.939041
Linear Subspace Learning-Based Dimensionality Reduction
Xudong Jiang
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TL;DR: Dimensionality reduction for pattern recognition using linear subspace learning. The goal is to discriminate class membership with minimum misclassification rate. High dimensional real-valued vectors are reduced to lower dimensional binary vectors.
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Abstract: The ultimate goal of pattern recognition is to discriminate the class membership of the observed novel objects with the minimum misclassification rate. An observed object is often represented by a high dimensional real-valued vector after some preprocessing while its class membership can be represented by a much lower dimensional binary vector. Thus, in the discriminating process, a pattern recognition system intrinsically reduces the dimensionality of the input data into the number of classes.
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Peripapillary Atrophy Detection by Sparse Biologically Inspired Feature Manifold
Jun Cheng,Dacheng Tao,Jiang Liu,Damon Wing Kee Wong,Ngan Meng Tan,Tien Yin Wong,Seang-Mei Saw +6 more
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An Investigation on the Accuracy of Truncated DKLT Representation for Speaker Identification With Short Sequences of Speech Frames
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•Journal Article
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John Wright,Yi Ma,Julien Mairal,Guillermo Sapiro,Thomas S. Huang,Shuicheng Yan +5 more
- 29 Apr 2010
TL;DR: This review paper highlights a few representative examples of how the interaction between sparse signal representation and computer vision can enrich both fields, and raises a number of open questions for further study.
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