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Consistency and Complementarity Jointly Regularized Subspace Support Vector Data Description for Multimodal Data
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DOI：10.1155/2024/1989706.png)
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Abstract
En 中文
The one-class classification (OCC) problem has always been a popular topic because it is difficult or expensive to obtain abnormal data in many practical applications. Most of OCC methods focused on monomodal data, such as support vector data description (SVDD) and its variants, while we often face multimodal data in reality. The data come from the same task in multimodal learning, and thus, the inherent structures among all modalities should be hold, which is called the consistency principle. However, each modality contains unique information that can be used to repair the incompleteness of other modalities. It is called the complementarity principle. To follow the above two principles, we designed a multimodal graph-regularized term and a sparse projection matrix-regularized term. The former aims to preserve the within-modal structural and between-modal relationships, while the latter aims to richly use the complementarity information hidden in multimodal data. Further, we follow the multimodal subspace (MS) SVDD architecture and use two regularized terms to regularize SVDD. Consequently, a novel OCC method for multimodal data is proposed, called the consistency and complementarity jointly regularized subspace SVDD (CCS-SVDD). Extensive experimental results demonstrate that our approach is more effective and competitive than other algorithms. The source codes are available at https://github.com/wongchuang/CCS_SVDD.
Keywords:
complementarity principle
consistency principle
multimodal data
one-class classification (OCC)
subspace learning
support vector data description (SVDD)
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