NDLI: Image Outlier Detection and Feature Extraction via L1-Norm-Based 2D Probabilistic PCA

Content Provider	IEEE Xplore Digital Library
Author	Fujiao Ju Yanfeng Sun Junbin Gao Yongli Hu Baocai Yin
Copyright Year	1992
Abstract	This paper introduces an L1-norm-based probabilistic principal component analysis model on 2D data (L1-2DPPCA) based on the assumption of the Laplacian noise model. The Laplacian or L1 density function can be expressed as a superposition of an infinite number of Gaussian distributions. Under this expression, a Bayesian inference can be established based on the variational expectation maximization approach. All the key parameters in the probabilistic model can be learned by the proposed variational algorithm. It has experimentally been demonstrated that the newly introduced hidden variables in the superposition can serve as an effective indicator for data outliers. Experiments on some publicly available databases show that the performance of L1-2DPPCA has largely been improved after identifying and removing sample outliers, resulting in more accurate image reconstruction than the existing PCA-based methods. The performance of feature extraction of the proposed method generally outperforms other existing algorithms in terms of reconstruction errors and classification accuracy.
Sponsorship	IEEE Signal Processing Society
Starting Page	4834
Ending Page	4846
Page Count	13
File Size	3102796
File Format	PDF
ISSN	10577149
Volume Number	24
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Data models Probabilistic logic Gaussian distribution Feature extraction Laplace equations Approximation algorithms Feature Extraction L1-Norm Probabilistic Principal Component Analysis Variational Bayesian Inference Outlier Detection feature extraction L1-norm probabilistic principal component analysis variational Bayesian inference outlier detection
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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