@inproceedings{13e09370452d4c6b958c8d2d0d040e39,
title = "Graph-based semi-supervised support vector data description for novelty detection",
abstract = "Support Vector Data Description (SVDD) is a well-known supervised learning method for novelty detection purpose. For its classification task, SVDD requires a fully-labeled dataset. Nonetheless, contemporary datasets always consist of a collection of labeled data samples jointly a much larger collection of unlabeled ones. This fact impedes the usage of SVDD in the real-world problems. In this paper, we propose to utilize the information implicated in a spectral graph to leverage SVDD in the context of semi-supervised learning. The theory and experiment evidence that the proposed method is able to efficiently employ the information carried in the spectral graph to not only enhance the generalization ability of SVDD but also enforce the cluster assumption which is crucial for a semi-supervised learning method",
keywords = "Kernel method, novelty detection, one-class classification, semi-supervised learning, subspace learning",
author = "Phuong Duong and Van Nguyen and Mi Dinh and Trung Le and Dat TRAN and Wanli MA",
year = "2015",
doi = "10.1109/ijcnn.2015.7280565",
language = "English",
isbn = "9781479919611",
volume = "1",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "1985--1990",
editor = "Amir Hussain",
booktitle = "2015 International joint conference on neural networks (IJCNN 2015)",
address = "United States",
note = "2015 International Joint Conference on Neural Networks, IJCNN 2015 ; Conference date: 12-07-2015 Through 17-07-2015",
}