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Ellipsoidal Subspace Support Vector Data Description
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DOI：10.1109/ACCESS.2020.3007123.png)
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摘要
En 中文
In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms data into a new subspace optimized for ellipsoidal encapsulation of target class data. We provide both linear and non-linear formulations for the proposed method. The method takes into account the covariance of the data in the subspace; hence, it yields a more generalized solution as compared to the data description in the subspace by hyperspherical encapsulation of target class data. We propose different regularization terms expressing the class variance in the projected space. We compare the results with classic and recently proposed one-class classification methods and achieve competing results and show clear improvement compared to the other support vector based methods. The proposed method is also noticed to converge much faster than recently proposed Subspace Support Vector Data Description.
Keyword:
Anomaly detection
ellipsoidal data description
machine learning
one-class classification
subspace learning
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期刊
IF:
3.6
论文数:
9.8W
被引数:
33.9W
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Global and local structure preserving sparse subspace learning: An iterative approach to unsupervised feature selection.png)
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