L1 norm based KPCA for novelty detection

Xiao, Yingchao; Wang, Huangang; Wang, Xu; Zhou, Junwu

doi:10.1016/j.patcog.2012.06.017

Cited by 35 publications

(18 citation statements)

References 16 publications

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“…This section shows the performance of the proposed KECA-L1 compared with the classical KECA [ 6 ] and OKECA [ 21 ] for real-world data classification using the SSL-based classifier illustrated in Section 3.3 . Several recent techniques such as PCA-L1 [ 27 ] and KPCA-L1 [ 28 ] are also included for comparison. The rationale to select these methods is that previous studies related to DR found that they can produce impressive results [ 27 – 29 ].…”

Section: Methodsmentioning

confidence: 99%

Kernel Entropy Component Analysis with Nongreedy L1-Norm Maximization

Huang

2018

Computational Intelligence and Neuroscience

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Kernel entropy component analysis (KECA) is a newly proposed dimensionality reduction (DR) method, which has showed superiority in many pattern analysis issues previously solved by principal component analysis (PCA). The optimized KECA (OKECA) is a state-of-the-art variant of KECA and can return projections retaining more expressive power than KECA. However, OKECA is sensitive to outliers and accused of its high computational complexities due to its inherent properties of L2-norm. To handle these two problems, we develop a new extension to KECA, namely, KECA-L1, for DR or feature extraction. KECA-L1 aims to find a more robust kernel decomposition matrix such that the extracted features retain information potential as much as possible, which is measured by L1-norm. Accordingly, we design a nongreedy iterative algorithm which has much faster convergence than OKECA's. Moreover, a general semisupervised classifier is developed for KECA-based methods and employed into the data classification. Extensive experiments on data classification and software defect prediction demonstrate that our new method is superior to most existing KECA- and PCA-based approaches. Code has been also made publicly available.

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Section: Methodsmentioning

confidence: 99%

Kernel Entropy Component Analysis with Nongreedy L1-Norm Maximization

Huang

2018

Computational Intelligence and Neuroscience

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“…The reconstruction error (12) is measured by the L2 norm distance betweenφ(x) and its projection onto the KPCs. It has been shown that the L2 norm is fragile in the presence of anomalies in the training data set and the L1 norm has better performance [63]. However, in TSML the model is constructed from training data that does not contain anomalies in the training data set, and therefore the L2 norm is used.…”

Section: Distance From the Manifoldmentioning

confidence: 99%

Univariate and Multivariate Time Series Manifold Learning

O'Reilly¹,

Moessner²,

Nati³

2017

Knowledge-Based Systems

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“…Reconstruction-based methods involve training a regression model with the training data [3,38,39]. The distance between the test vector and the output of the system (i.e.…”

Section: Other Categoriesmentioning

confidence: 99%