Weighted Neighborhood Preserving Ensemble Embedding

被引:7
|
作者
Mehta, Sumet [1 ,2 ]
Zhan, Bi-Sheng [1 ]
Shen, Xiang-Jun [1 ]
机构
[1] Jiangsu Univ, Sch Comp Sci & Commun Engn, Zhenjiang 212013, Jiangsu, Peoples R China
[2] JCDM Coll Engn, Dept Elect & Commun Engn, Sirsa 125055, Haryana, India
来源
ELECTRONICS | 2019年 / 8卷 / 02期
基金
中国国家自然科学基金;
关键词
dimensional reduction; graph construction; machine learning; neighborhood preserving embedding; face recognition; DIMENSIONALITY REDUCTION; FACE RECOGNITION; ILLUMINATION; PROJECTIONS; EIGENFACES; ALGORITHM;
D O I
10.3390/electronics8020219
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Neighborhood preserving embedding (NPE) is a classical and very promising supervised dimensional reduction (DR) technique based on a linear graph, which preserves the local neighborhood relations of the data points. However, NPE uses the K nearest neighbor (KNN) criteria for constructing an adjacent graph which makes it more sensitive to neighborhood size. In this article, we propose a novel DR method called weighted neighborhood preserving ensemble embedding (WNPEE). Unlike NPE, the proposed WNPEE constructs an ensemble of adjacent graphs with the number of nearest neighbors varying. With this graph ensemble building, WNPEE can obtain the low-dimensional projections with optimal embedded graph pursuing in a joint optimization manner. WNPEE can be applied in many machine learning fields, such as object recognition, data classification, signal processing, text categorization, and various deep learning tasks. Extensive experiments on Olivetti Research Laboratory (ORL), Georgia Tech, Carnegie Mellon University-Pose and Illumination Images (CMU PIE) and Yale, four face databases demonstrate that WNPEE achieves a competitive and better recognition rate than NPE and other comparative DR methods. Additionally, the proposed WNPEE achieves much lower sensitivity to the neighborhood size parameter as compared to the traditional NPE method while preserving more of the local manifold structure of the high-dimensional data.
引用
收藏
页数:18
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