Robust support vector data description for novelty detection with contaminated data

Wang, Kunzhe; Lan, Haibin

doi:10.1016/j.engappai.2020.103554

Cited by 21 publications

(5 citation statements)

References 24 publications

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“…Solutions to one-class classification and novelty detection either estimate the density of the inlier distribution [10,11] or determine a geometric property of the inliers, such as their boundary set [12][13][14][15]. When the inlier distribution is nicely approximated by a low-dimensional linear subspace, [16] proposes to distinguish between inliers and outliers via Principal Component Analysis (PCA).…”

Section: Previous Workmentioning

confidence: 99%

“…If there are also outliers (with a simple shape) among the inliers (with a complex shape), encoding the inlier distribution becomes even more difficult. Nevertheless, some previous works already explored the possibility of corrupted training set [14,15,19]. In particular, [14,19] test artificial instances with at most 5% corruption of the training set and [15] considers ratios of 10%, but with very small numbers of training points.…”

Section: Previous Workmentioning

confidence: 99%

“…Nevertheless, some previous works already explored the possibility of corrupted training set [14,15,19]. In particular, [14,19] test artificial instances with at most 5% corruption of the training set and [15] considers ratios of 10%, but with very small numbers of training points. In this work we consider corruption ratios up to 30%, with a method that tries to estimate the distribution of the training set, and not just a geometric property.…”

Section: Previous Workmentioning

confidence: 99%

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Novelty Detection via Robust Variational Autoencoding

Lai

Zou

2020

Preprint

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We propose a new method for novelty detection that can tolerate nontrivial corruption of the training points. Previous works assumed either no or very low corruption. Our method trains a robust variational autoencoder (VAE), which aims to generate a model for the uncorrupted training points. To gain robustness to corruption, we incorporate three changes to the common VAE: 1. Modeling the latent distribution as a mixture of Gaussian inliers and outliers, while using only the inlier component when testing; 2. Applying the Wasserstein-1 metric for regularization, instead of Kullback-Leibler divergence; and 3. Using a least absolute deviation error for reconstruction, which is equivalent to assuming a heavy-tailed likelihood. We illustrate state-of-the-art results on standard benchmark datasets for novelty detection. * Equal contribution.Preprint. Under review.

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

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Novelty Detection via Robust Variational Autoencoding

Lai

Zou

2020

Preprint

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“…The one-class support vector machine (OC-SVM) 12 and support vector data description (SVDD) 13,14 are two most common OCC algorithms. A support vector machine (SVM) was proposed, 15 which can well solve the binary classification problems of small sample size and high sample dimension.…”

Section: Introductionmentioning

confidence: 99%

Soft one-class extreme learning machine for turboshaft engine fault detection

Zhao

Huang

2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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One-class extreme learning machine (OC-ELM) is a very common one-class classification algorithm. In the process of constructing the OC-ELM, a part of training samples will be removed by the algorithm, which leads to inconsistency between its constraint condition and decision function, and is also known as the problem of hard margin flaw. In order to solve this problem, a soft one-class extreme learning machine (SOC-ELM) is proposed by assigning an appropriate margin to each training sample. Experimental results on benchmark data sets show that SOC-ELM has a strong classification performance. When the SOC-ELM is used for fault detection of a turboshaft engine, it can achieve good detection effectiveness and has strong robustness.

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“…To reduce the impact of outliers on modeling in single-mode process, Chen et al [23] proposed robust-SVDD by introducing cutoff-distance-based local density of each data sample and the ε-insensitive loss function with negative samples. Wang and Lan [24] used the SD outlyingness to assign lower weight values to outliers. For multimode process, Zhao et al utilized the weighted mean and standard deviation of each sample's neighbors to standardize the dataset and applied weighted local standardization (WLS) strategy to wSVDD [25].…”

Section: Introductionmentioning

confidence: 99%

SPA‐Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring

et al. 2021

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Many industrial processes are operated in multiple modes due to different manufacturing strategies. Multimodality of process data is often accompanied with nonlinear and non-Gaussian characteristics, which makes data-driven monitoring more complicated. In this paper, statistics pattern analysis (SPA) is introduced to extract low- and high-order statistics from raw process data. Support vector data description (SVDD), which can deal with nonlinear and non-Gaussian problems, is applied to monitor multimode process in this paper. To improve detection performance of SVDD for training multimode data with outliers, modified local reachability density ratio (mLRDR) is proposed as a weight factor to be embedded in the weighted-SVDD (wSVDD) model, in which the local neighbors in terms of both space and time are considered. Finally, the effectiveness and superiority of our proposed method are demonstrated by the Tennessee-Eastman (TE) process and wastewater treatment process (WWTP).

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Robust support vector data description for novelty detection with contaminated data

Cited by 21 publications

References 24 publications

Novelty Detection via Robust Variational Autoencoding

Novelty Detection via Robust Variational Autoencoding

Soft one-class extreme learning machine for turboshaft engine fault detection

SPA‐Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring

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