A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefficient and inherently difficult to apply at large scales. In the era of big data, the computational issue becomes critical. To fill this gap, we propose a large-scale MVSC (LMVSC) algorithm with linear order complexity. Inspired by the idea of anchor graph, we first learn a smaller graph for each view. Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. Interestingly, it turns out that our model also applies to single-view scenario. Extensive experiments on various large-scale benchmark data sets validate the effectiveness and efficiency of our approach with respect to state-of-the-art clustering methods.

To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying latent representation and performs graph learning and label propagation based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict the data more comprehensively than every single view individually, accordingly making the graph more accurate and robust as well. Finally, LMSSC integrates latent representation learning, graph construction, and label propagation into a unified framework, which makes each subtask optimized. Experimental results on real-world benchmark datasets validate the effectiveness of our proposed method.

In this paper, the novel flow control strategy called dimensional bubble flow control (DBFC) is presented. The flow control strategy of DBFC builds on virtual cut-through switching and credit-based flow control mechanism and analyzes the credit value of port and the routing information of the packets to realize the point-point flow control. In the 2-D mesh network on chip, when the flow control strategy of DBFC is accepted, the adaptive dimensional bubble routing (ADBR) algorithm designed in this paper can get the goals including deadlock-free and minimal distance even if the cyclic dependencies exist. In this paper, the detail proof is provided for these conclusions. Lastly, we adapt the source code of NOXIM that is a popular simulator of on-chip networks and realize the flow control of DBFC and ADBR algorithm in NOXIM. We test the performance of ADBR on NOXIM. The simulation performance shows our scheme is superior to the usual approach such as XY dimension-order routing, with nearly 17.5% improvement in the packets latency and throughput.