Preserving Global and Local Structures for Supervised Dimensionality Reduction
School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
2 Department of Information Technology, Clayton State University, Morrow, GA, 30260, China
a Corresponding author: email@example.com
In this paper, we develop a new approach for dimensionality reduction of labeled data. This approach integrates both global and local structures of data into a new objective, we show that the objective can be optimized by solving an eigenvalue problem. Testing results on benchmark data sets show that this new approach can effectively capture both the crucial global and local structures of data and thus lead to more accurate results for dimensionality reduction than existing approaches.
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