http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=108&id=1140
Hiroyuki Yoda, Akira Imakura, Momo Matsuda, Xiucai Ye, Tetsuya Sakurai · International Journal of Machine Learning and Computing · 2020
Novelty detection represents the detection of anomalous data based on a training set consisting of only the normal data.In this study, we propose a new probabilistic approach for novelty detection to effectively detect anomalous data, particularly for the case of multimodal training dataset.Our method is inspired by the Least-Squares Probabilistic Classifier (LSPC), which is an efficient multi-class classification method.Numerical experimental results based on multimodal datasets show that the proposed method outperforms the related methods.