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AEKOC+: Kernel Ridge Regression-Based Auto-Encoder for One-Class Classification Using Privileged Information

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Abstract

In recent years, non-iterative learning approaches for kernel have received quite an attention by researchers and kernel ridge regression (KRR) approach is one of them. Recently, KRR-based Auto-Encoder is developed for the one-class classification (OCC) task and named as AEKOC. OCC is generally used for outlier or novelty detection. The brain can detect outlier just by learning from only normal samples. Similarly, OCC also uses only normal samples to train the model, and trained model can be used for outlier detection. In this paper, AEKOC is enabled to utilize privileged information, which is generally ignored by AEKOC or any traditional machine learning technique but usually present in human learning. For this purpose, we have combined learning using privileged information (LUPI) framework with AEKOC, and proposed a classifier, which is referred to as AEKOC+. Privileged information is only available during training but not during testing. Therefore, AEKOC is unable to utilize this information for building the model. However, AEKOC+ can efficiently handle the privileged information due to the inclusion of the LUPI framework with AEKOC. Experiments have been conducted on MNIST dataset and on various other datasets from UCI machine learning repository, which demonstrates the superiority of AEKOC+ over AEKOC. Our formulation shows that AEKOC does not utilize the privileged features in learning; however, formulation of AEKOC+ helps it in learning from the privileged features differently from other available features and improved generalization performance of AEKOC. Moreover, AEKOC+ also outperformed two LUPI framework–based one-class classifiers (i.e., OCSVM+ and SSVDD+).

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Notes

  1. Some researchers [45, 46] followed the name of kernel extreme learning machine (KELM) [49,50,51], and some researchers followed the name of KRR [42, 47] instead of KELM. We do not want to go in the debate of the naming convention. Since there are no differences in the final solution of KELM and KRR, we decided to follow the traditional name KRR instead of KELM. However, we kept both names in this paper to avoid the confusion, i.e., KRR/KELM-based Auto-Encoder for OCC is referred to as AEKOC/AAKELM in this paper.

  2. https://github.com/Chandan-IITI

  3. https://github.com/Chandan-IITI/svmplus_matlab/tree/master/data

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Funding

This research is supported by the Department of Electronics and Information Technology (DeITY, Govt. of India) under Visvesvaraya PhD scheme for electronics & IT. This work is also supported by Science and Engineering Research Board (SERB) funded Research Project, Government of India under Early Career Research Award Scheme, Grant No. ECR/2017/000053 and Council of Scientific & Industrial Research (CSIR), New Delhi, INDIA under Extra Mural Research (EMR) Scheme grant no. 22(0751)/17/EMR-II. We gratefully acknowledge the Indian Institute of Technology Indore for providing facilities and support.

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Correspondence to Chandan Gautam.

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Gautam, C., Tiwari, A. & Tanveer, M. AEKOC+: Kernel Ridge Regression-Based Auto-Encoder for One-Class Classification Using Privileged Information. Cogn Comput 12, 412–425 (2020). https://doi.org/10.1007/s12559-019-09705-4

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