*O*(n

^{3}) to

*O*(n) (where n is the number of samples). However, LFMVC also limits the search space of the optimal solution, meaning that the clustering results obtained are not ideal. Accordingly, in order to obtain more information from each base partition and thus improve the clustering performance, we propose a new late fusion multi-view clustering algorithm with a computational complexity of

*O*(n

^{2}). Experiments on several commonly used datasets demonstrate that the proposed algorithm can reach quickly convergence. Moreover, compared with other late fusion algorithms with computational complexity of

*O*(n), the actual time consumption of the proposed algorithm does not significantly increase. At the same time, comparisons with several other state-of-the-art algorithms reveal that the proposed algorithm also obtains the best clustering performance. 相似文献

**总被引：1，自引：0，他引：1**The results indicated that the parametric variations can lead to an improvement in the performance but high levels of human interaction are required to make a combined improvement to the performance. The use of genetic algorithms provided an efficient method of searching the design space for an optimal joint. The single objective function provides an excellent reduction in the weight and maintaining or improving the performance of the joint to in-plane compressive loading. The use of the multi-objective function whereby a weighting was applied to the weight, stress and stiffness performance criteria proved extremely successful in further optimising the joint. The use of genetic algorithms has been demonstrated to efficiently search the complex design space of a structural connection and the use of multi-objective functions as the most effective selection method. 相似文献

This paper proposes a collaborative filtering method for massive datasets that is based on Bayesian networks. We first compare the prediction accuracy of four scoring-based learning Bayesian networks algorithms (AIC, MDL, UPSM, and BDeu) and two conditional-independence-based (Cl-based) learning Bayesian networks algorithms (MWST, and Polytree-MWST) using actual massive datasets. The results show that (1) for large networks, the scoring-based algorithms have lower prediction accuracy than the CI-based algorithms and (2) when the scoring-based algorithms use a greedy search to learn a large network, algorithms which make a lot of arcs tend to have less prediction accuracy than those that make fewer arcs. Next, we propose a learning algorithm based on MWST for collaborative filtering of massive datasets. The proposed algorithm employs a traditional data mining technique, the “a priori” algorithm, to quickly calculate the amount of mutual information, which is needed in MWST, from massive datasets. We compare the original MWST algorithm and the proposed algorithm on actual data, and the comparison shows the effectiveness of the proposed algorithm.

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