NDLI: Collaborative representation-based locality preserving projections for image classification

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Collaborative representation-based locality preserving projections for image classification

Content Provider	IET Digital Library
Author	Gou, Jianping Yang, Yuanyuan Liu, Yong Chao Yuan, Yunhao Du, Lan Yang, Hebiao
Abstract	Graph embedding has attracted much more research interests in dimensionality reduction. In this study, based on collaborative representation and graph embedding, the authors propose a new linear dimensionality reduction method called collaborative representation-based locality preserving projection (CRLPP). In the CRLPP, they assume that the similar samples should have similar reconstructions by collaborative representation and the similar reconstructions should also have the similar low-dimensional representations in the projected subspace. CRLPP first reconstructs each training sample using the collaborative representation of the other remaining training samples, and then designs the graph construction of all training samples, finally establishes the objective function of graph embedding using the collaborative reconstructions and the constructed graph. The proposed CRLPP can well preserve the intrinsic geometrical and discriminant structures of high-dimensional data in low-dimensional subspace. The effectiveness of the proposed is verified on several image datasets. The experimental results show that the proposed method outperforms the state-of-art dimensionality reduction.
Starting Page	310
Ending Page	315
Page Count	6
Volume Number	2020
e-ISSN	20513305
Issue Number	Issue 13, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2020/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1172
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-01-13
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Collaborative Reconstructions Collaborative Representation-based Locality Combinatorial Mathematics Computer Vision And Image Processing Technique Constructed Graph CRLPP Feature Extraction Graph Construction Graph Embedding Graph Theory High-dimensional Data Image Classification Image Recognition Image Representation Knowledge Engineering Technique Learning in AI Linear Dimensionality Reduction Method Low-dimensional Representations Low-dimensional SubSpace Projected SubSpace Reconstructs Each Training Sample Remaining Training Sample Similar Reconstructions Similar Sample State-of-art Dimensionality Reduction
Content Type	Text
Resource Type	Article

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