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Multi-view subspace clustering has been an important and powerful tool for partitioning multi-view data, especially multi-view high-dimensional data. Despite great success, most of the existing multi-view subspace clustering methods still suffer from three limitations. First, they often recover the subspace structure in the original space, which can not guarantee the robustness when handling multi-view data with nonlinear structure. Second, these methods mostly regard subspace clustering and affinity matrix learning as two independent steps, which may not well discover the latent relationships among data samples. Third, many of them ignore the different importance of multiple views, whose performance may be badly affected by the low-quality views in multi-view data. To overcome these three limitations, this paper develops a novel subspace clustering method for multi-view data, termed Kernelized Multi-view Subspace Clustering via Auto-weighted Graph Learning (KMSC-AGL). Specifically, the proposed method implicitly maps the multi-view data from linear space into nonlinear space via kernel-induced functions, so as to exploit the nonlinear structure hidden in data. Furthermore, our method aims to enhance the clustering performance by learning a set of view-specific representations and their affinity matrix in a general framework. By integrating the view weighting strategy into this framework, our method can automatically assign the weights to different views, while learning an optimal affinity matrix that is well-adapted to the subsequent spectral clustering. Extensive experiments are conducted on a variety of multi-view data sets, which have demonstrated the superiority of the proposed method.

The multistep-ahead prediction of chlorophyll provides an effective means for early warning of red tide. However, since multistep-ahead forecasting presents challenges, such as vague interactive relationships among ocean factors, long-term dependence modeling, and accumulative errors, existing methods mostly concentrate on the current time or one-step-ahead forecasting. In this paper, a hierarchical multistep-ahead forecasting model spatial-temporal attention network(STAN), which integrates the spatial context extractor network(SCE-net), long short-term memory network(LSTM), and the temporal attention mechanism, is proposed for the prediction of chlorophyll. In STAN, the input layer utilizes SCE-net to excavate relationships among ocean factors and generate high-level semantic via embedding factors into a continuous low-dimensional space. The middle layer applies LSTM to build the long-term dependencies of corresponding semantic representations. The output layer uses another LSTM with temporal attention to reduce accumulative errors and maintain temporal continuity. The attention can assign different weights to the middle layer’s hidden state and generate a context vector. Then the context vector and the final predicted value are considered as the current input for better forecasting. The buoy observation data of the Xiamen coastal area monitored in 2009–2011 is used to verify the efficiency of STAN. Experimental results prove that STAN outperforms the state-of-the-art methods of multistep-ahead prediction. When using 7 observation steps to forecast 15 steps, the MAPE of STAN is 0.3209, and the MAE is 0.1 lower than the values of the baselines approaches.