NDLI: Locally Linear Embedding algorithm based on OMP for incremental learning

Content Provider	IEEE Xplore Digital Library
Author	Yiqin Leng Li Zhang Jiwen Yang
Copyright Year	2014
Description	Author affiliation: Provincial Key Lab. for Comput. Inf. Process. Technol., Soochow Univ., Suzhou, China (Yiqin Leng; Li Zhang; Jiwen Yang)
Abstract	Locally Linear Embedding (LLE) is a sort of powerful nonlinear dimensionality reduction algorithms. The basic idea behind the LLE method is that each data point and its neighbors lie on or close to a locally linear patch of the manifold if there is sufficient data. Then the local geometry of these patches is described by using linear coefficients which can reconstruct each data point from its neighbors. However, LLE operates in a batch way and its dimension reduction cannot be generalized to unseen samples. If a test sample arrives, LLE must run repeatedly and the former computational results are discarded. Thus, some incremental methods have been proposed for LLE to solve this problem. In these incremental methods, the neighbor number is globally fixed, which may result in selecting points from another linear space as neighbors. This paper presents LLE based on orthogonal matching pursuit (OMP) and applies it to classification tasks. In the classification tasks, dimensionality reduction on test samples is implemented by applying dimension reduction on training samples. The new LLE method could select a more appropriate neighbors from the selected neighbors. OMP is applied to not only LLE for training samples, but also the incremental learning of LLE for test samples. Compared with other linear incremental methods, experimental results show that the proposed method is promising.
Starting Page	3100
Ending Page	3107
File Size	591888
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889460
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Joints Conferences Neural networks
Content Type	Text
Resource Type	Article

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