Multi-view Clustering Based on Deep Non-negative Tensor Factorization

Wei Feng, Dongyuan Wei, Qianqian Wang, Bo Dong, Quanxue Gao

Abstract:

Multi-view clustering (MVC) methods based on non-negative matrix factorization (NMF) have gained popularity owing to their ability to provide interpretable clustering results. However, these NMF-based MVC methods generally process each view independently and thus ignore the potential relationship between views.
Besides, they are limited in the ability to capture the nonlinear data structures.
To overcome these weaknesses and inspired by deep learning, we propose a multi-view clustering method based on deep non-negative tensor factorization (MVC-DNTF). With deep tesnor factorization, our method can well exploit the spatial structure of the original data and is capable of extracting more deep and nonlinear features embedded in different views. To further extract the complementary information of different views, we adopt the weighted tensor Schatten $p$-norm regularization term. An optimization algorithm is developed to effectively solves the MVC-DNTF objective. Extensive experiments are performed to demonstrate the effectiveness and superiority of our method.

Primary Subject Area: [Content] Multimodal Fusion

Relevance To Conference: In this work, we propose a multi-view clustering method based on deep non-negative tensor factorization. The development of sensing technology makes the massive and complex multimedia data collected, resulting in challenges to obtain high-quality data annotations at a low cost. Nevertheless, our method can be effectively applied to the preprocessing of unlabeled multimedia data, which is beneficial to the subsequent multimedia data processing. Specifically, by leveraging deep tensor decomposition, our method achieves better clustering performance and hence shows significance in multimedia data processing.

Submission Number: 3857