Details

Autor(en) / Beteiligte

• Li, Ruihuang
• Zhang, Changqing
• Fu, Huazhu
• Peng, Xi
• Zhou, Joey Tianyi
• Hu, Qinghua

Titel

Reciprocal Multi-Layer Subspace Learning for Multi-View Clustering

Ist Teil von

• 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, p.8171-8179

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Multi-view clustering is a long-standing important research topic, however, remains challenging when handling high-dimensional data and simultaneously exploring the consistency and complementarity of different views. In this work, we present a novel Reciprocal Multi-layer Subspace Learning (RMSL) algorithm for multi-view clustering, which is composed of two main components: Hierarchical Self-Representative Layers (HSRL), and Backward Encoding Networks (BEN). Specifically, HSRL constructs reciprocal multi-layer subspace representations linked with a latent representation to hierarchically recover the underlying low-dimensional subspaces in which the high-dimensional data lie; BEN explores complex relationships among different views and implicitly enforces the subspaces of all views to be consistent with each other and more separable. The latent representation flexibly encodes complementary information from multiple views and depicts data more comprehensively. Our model can be efficiently optimized by an alternating optimization scheme. Extensive experiments on benchmark datasets show the superiority of RMSL over other state-of-the-art clustering methods.