Multimodal Subspace Support Vector Data Description
This work addresses the challenge of integrating diverse data sources for anomaly detection, representing an incremental improvement in multimodal learning techniques.
The paper tackles the problem of one-class classification with multimodal data by projecting data from multiple modalities into an optimized common subspace, achieving superior performance over competing methods in four out of five datasets.
In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively transforms the data from the original feature space of each modality to a new common feature space along with finding a joint compact description of data coming from all the modalities. For data in each modality, we define a separate transformation to map the data from the corresponding feature space to the new optimized subspace by exploiting the available information from the class of interest only. We also propose different regularization strategies for the proposed method and provide both linear and non-linear formulations. The proposed Multimodal Subspace Support Vector Data Description outperforms all the competing methods using data from a single modality or fusing data from all modalities in four out of five datasets.