Weighted Neighborhood Preserving Ensemble Embedding

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...

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Bibliographic Details
Published in:Electronics (Basel) 2019-02, Vol.8 (2), p.219
Main Authors: Mehta, Sumet, Zhan, Bi-Sheng, Shen, Xiang-Jun
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Cognitive tasks
Data points
Deep learning
Direct reduction
Discriminant analysis
Eigenvalues
Embedding
Machine learning
Methods
Neighborhoods
Object recognition
Optimization
Parameter sensitivity
Principal components analysis
Signal classification
Signal processing
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Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics8020219