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Hypergraph is popularly used for describing multi-relationships among objects in a unified manner, and spectral clustering is regarded as one of the most effective algorithms for partitioning those objects (vertices) into different communities. However, the traditional spectral clustering for hypergraph (HC) incurs expensive costs in terms of both time and space. In this paper, we propose a framework called GraphLSHC to tackle the scalability problem faced by the large scale hypergraph spectral clustering. In our solution, the hypergraph used in GraphLSHC is expanded into a general format to capture complicated higher-order relationships. Moreover, GraphLSHC is capable to simultaneously partition both vertices and hyperedges according to the "eigen-trick", which provides an approach for reducing the computational complexity of the clustering. To improve the performance further, several hyperedge-based sampling techniques are proposed, which can supplement the sampled matrix with the whole graph information. We also give a theoretical guarantee for the error boundary of the supplement. Several experiments show the superiority of the proposed framework over the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Graphics Processing Units (GPUs) are effective tools for improving the efficiency of many computationally demanding algorithms. GPUs have been particularly effective at speeding up the evaluation stage of evolutionary machine learning systems. The speedups obtained in these tasks, depend on many factors: dataset characteristics, the parallel strategy of the GPU code and the fit of the GPU code within the rest of the learning system. A solid understanding of all these factors is required to choose and adjust the most suitable GPU strategy in different scenarios. In this paper we present a large-scale performance evaluation of two GPU strategies for speeding up the evaluation of evolutionary machine learning systems. We use highly-tuneable synthetic problems to exhaustively explore the space of problem characteristics and determine the type of problems where each strategy performs best. The lessons learnt from this extensive evaluation are further confirmed by running experiments on a broad range of real-world datasets. Through this thorough evaluation we obtain a solid understanding of the capabilities and limitations of the evaluated GPU strategies for boosting the efficiency of evolutionary machine learning systems. [ABSTRACT FROM AUTHOR]

Multiple Kernel Learning (MKL) can boost classification performance through using multiple kernels rather than a single fixed one. Unlike the traditional MKL with the implicit kernels, Multiple Empirical Kernel Learning (MEKL) explicitly maps input data into multiple feature spaces. This paper focuses on MEKL and proposes an effective Threefold Structural MEKL (TSMEKL). The first fold structure is the space structural information between different mapped feature spaces. The second one is the class discriminant information within each mapped feature space. The third one is the cluster structural information of samples in each mapped feature space. The classical MEKL mainly pays attention to the first two structures, but neglects the last one. The proposed TSMEKL introduces the cluster structural information into MEKL. Doing so can simultaneously utilize the space, the class, and the cluster information in the way from globality to locality. Therefore, TSMEKL utilizes threefold structural information to result in the improvement of classification performance. To the best of our knowledge, it is the first time to introduce the cluster information into the MEKL framework. The main advantage of the developed TSMEKL is considering different folds of data information to improve classification performance. The experimental results validate the feasibility and effectiveness of TSMEKL. Moreover, we discuss the theoretical and experimental generalization risk bound of the proposed algorithm in terms of the Rademacher complexity. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper presents a new multiobjective swarm hybrid learning algorithm, MPSON. [•] We also propose MGAN to comparison with MPSON based on performance measures. [•] The aim of our algorithms is to obtain simple and accurate RBF networks simultaneously. [•] We validate the algorithms on datasets with different features, samples and classes. [•] Results show MPSON is the best algorithm with the accuracy-complexity balance for RBF network design. [Copyright &y& Elsevier]

Abstract: In this paper, we analyse the single processor total weighted completion time scheduling problem with the learning effect, where the processing time of each job is a non-increasing function dependent on the sum of the normal processing times of preceding jobs. We prove that the considered problem is at least NP-hard. Moreover, a pseudopolynomial time dynamic programming algorithm that optimally solves the problem with a step learning function (curve) is constructed. Furthermore, fast approximation algorithms for the general version of the problem, where job processing times are described by arbitrary functions dependent on the sum of the normal job processing times, are provided. Their efficiency is verified numerically and for Weighted Shortest Processing Times algorithm a worst case analysis is also performed. [Copyright &y& Elsevier]