Multiview clustering (MVC) collects complementary and abundant information, which draws much attention in machine learning and data mining community. Existing MVC methods usually hold the assumption that all the views are complete. However, multiple source data are often incomplete in real‐world applications, and so on sensor failure or unfinished collection process, which gives rise to incomplete multiview clustering (IMVC). Although enormous efforts have been devoted in IMVC, there still are some urgent issues that need to be solved: (i) The locality among multiple views has not been utilized in the existing mechanism; (ii) Existing methods inappropriately force all the views to share consensus representation while ignoring specific structures. In this paper, we propose a novel method termed partial MVC with locality graph regularization to address these issues. First, followed the traditional IMVC approaches, we construct weighted semi‐nonnegative matrix factorization models to handle incomplete multiview data. Then, upon the consensus representation matrix, the locality graph is constructed for regularizing the shared feature matrix. Moreover, we add the coefficient regression term to constraint the various base matrices among views. We incorporate the three aforementioned processes into a unified framework, whereas they can negotiate with each other serving for learning tasks. An effective iterative algorithm is proposed to solve the resultant optimization problem with theoretically guaranteed convergence. The comprehensive experiment results on several benchmarks demonstrate the effectiveness of the proposed method.

Fuel prices, which are of broad concern to the general public, are always seen as a challenging research topic. This paper proposes a variational Bayesian structural time-series model (STM) to effectively process complex fuel sales data online and provide real-time forecasting of fuel sales. While a traditional STM normally uses a probability model and the Markov chain Monte Carlo (MCMC) method to process change points, using the MCMC method to train the online model can be difficult given a relatively heavy computing load and time consumption. We thus consider the variational Bayesian STM, which uses variational Bayesian inference to make a reliable judgment of the trend change points without relying on artificial prior information, for our prediction method. With the inferences being driven by the data, our model passes the quantitative uncertainties to the forecast stage of the time series, which improves the robustness and reliability of the model. After conducting several experiments by using a self-collected dataset, we show that compared with a traditional STM, the proposed model has significantly shorter computing times for approximate forecast precision. Moreover, our model improves the forecast efficiency for fuel sales and the synergy of the distributed forecast platform based on an architecture of network.

No abstract

No abstract