Efficient Multi-View Clustering via Unified and Discrete Bipartite Graph Learning.

Although previous graph-based multi-view clustering (MVC) algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from high computational complexity, which restricts their applications in large-scale scenarios. Second, they usually perform graph learning either at the single-view level or at the view-consensus level, but often neglect the possibility of the joint learning of single-view and consensus graphs. Third, many of them rely on the k -means for discretization of the spectral embeddings, which lack the ability to directly learn the graph with discrete cluster structure. In light of this, this article presents an efficient MVC approach via unified and discrete bipartite graph learning (UDBGL). Specifically, the anchor-based subspace learning is incorporated to learn the view-specific bipartite graphs from multiple views, upon which the bipartite graph fusion is leveraged to learn a view-consensus bipartite graph with adaptive weight learning. Furthermore, the Laplacian rank constraint is imposed to ensure that the fused bipartite graph has discrete cluster structures (with a specific number of connected components). By simultaneously formulating the view-specific bipartite graph learning, the view-consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size. Experiments on a variety of multi-view datasets demonstrate the robustness and efficiency of our UDBGL approach. The code is available at https://github.com/huangdonghere/UDBGL.

Mechanical behaviors of tension and relaxation of tongue and soft palate: Experimental and analytical modeling.

This study is to characterize mechanical properties of uniaxial tension and stress relaxation responses of muscle tissues of tongue and soft palate. Uniaxial tension test and stress relaxation test on 39 fresh tissue samples from four five-month-old boars (65 ±â€¯15 kg) are conducted. Firstly, the rationality of the samples' dimension design and experimenal data measurement is validated by one-way ANOVA F-type test. Mechanical properties, including stress-strain relationship and stress relaxation characteristic, are then investigated in details to show the nonlinear behaviors of the tissue samples clearly. Finally, a constitutive model of representing the mechanical properties is formulated within the nonlinear integral representation framework proposed by Pinkin and Rogers, and corresponding material parameters are fitted to the experimental data based on the Levenberg-Marquardt minimization algorithm. The results of the fitting comparison prove that the formulated constitutive model can capture the observed nonlinear behaviors of the muscle tissue samples in both the axial tension and stress relaxation experiments.