NDLI: A linear subspace learning algorithm for incremental data

Content Provider	IEEE Xplore Digital Library
Author	Bin Fang Jing Chen Yuan-Yan Tang
Copyright Year	2009
Description	Author affiliation: School of Computer, Chongqing University, 400030, China (Bin Fang; Jing Chen; Yuan-Yan Tang)
Abstract	Incremental learning has attracted increasing attention in the past decade. Since many real tasks are high-dimensional problems, dimensionality reduction is the important step. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two of the most widely used dimensionality reduction algorithms. However, PCA is an unsupervised algorithm. It is known that PCA is not suitable for classification tasks. Generally, LDA outperforms PCA when classification problem is involved. However, the major shortcoming of LDA is that the performance of LDA is degraded when encountering singularity problem. Recently, the modified LDA, Maximum margin criterion (MMC) was proposed to overcome the shortcomings of PCA and LDA. Nevertheless, MMC is not suitable for incremental data. The paper proposes an incremental extension version of MMC, called Incremental Maximum margin criterion (IMMC) to update projection matrix when new observation is coming, without repetitive learning. Since the approximation intermediate eigenvalue decomposition is introduced, it is low in computational complexity.
Starting Page	101
Ending Page	106
File Size	264312
Page Count	6
File Format	PDF
ISBN	9781424437283
DOI	10.1109/ICWAPR.2009.5207464
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Incremental Maximum margin criterion (IMMC) Dimensionality reduction Maximum margin criterion (MMC) Scattering Wavelet analysis Pattern recognition Matrix decomposition Computational complexity Incremental learning Linear discriminant analysis (LDA) Feature extraction Eigenvalues and eigenfunctions Linear discriminant analysis Principal component analysis
Content Type	Text
Resource Type	Article

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