{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:54:50Z","timestamp":1777704890102,"version":"3.51.4"},"reference-count":30,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>Aiming at anomaly detection upon a high-dimensional space, this paper proposed a novel autoencoder-support vector machine. The key thought is that using the autoencoder extracts the features from high-dimensional data, and then the support vector machine achieves the separation of abnormal features and normal features. To increase the precision of identifying anomalies, Chebyshev\u2019s theorem was used to estimate the upper of the number of abnormal features. Meanwhile, the dot product operation was implemented in order to strengthen the learning of the model for class labels. Experiment results show that the detected accuracy of the proposed method is 0.766 when the data dimensionality is 5408, and also wins over competitors in detected performance for the considered cases. We also demonstrate that the strengthened learning of class labels can improve the ability of the model to detect anomalies. In terms of noise resistance and overcoming the curse of dimensionality, the former can carry out more efforts than the latter.<\/jats:p>","DOI":"10.3233\/jifs-231735","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T12:11:45Z","timestamp":1690546305000},"page":"9457-9469","source":"Crossref","is-referenced-by-count":3,"title":["Anomaly detection for high-dimensional data using a novel autoencoder-support vector machine"],"prefix":"10.1177","volume":"45","author":[{"given":"Zhuo","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Big Data and Software Engineering, Chongqing University, Chongqing, China"},{"name":"Chongqing Expressway Group Co., Ltd., Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science College of Software, Southwest University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongbin","family":"Wang","sequence":"additional","affiliation":[{"name":"Chongqing Expressway Group Co., Ltd., Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"5","key":"10.3233\/JIFS-231735_ref3","doi-asserted-by":"crossref","first-page":"2845","DOI":"10.1109\/TCSVT.2021.3099846","article-title":"TCDesc: Learning topology consistent descriptors for image matching","volume":"32","author":"Pan","year":"2022","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"10.3233\/JIFS-231735_ref4","doi-asserted-by":"crossref","unstructured":"Pan H. , Bai Y. , He Z. and Zhang C. , AAGCN: Adjacency-aware graph convolutional network for person re-identification, Knowl. 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