论文标题
Simplemkm：简单多个内核K-均值
SimpleMKKM: Simple Multiple Kernel K-means
论文作者
论文摘要
我们提出了一种简单而有效的多个内核聚类算法，称为简单的多核K-均值（Simplemkkm）。它将广泛使用的监督内核对准标准扩展到多内核聚类。我们的标准是由内核系数和聚类分区矩阵中的可怕的最小化最大化问题给出的。为了优化它，我们将问题重新构建为平滑的最小化，可以使用降低的梯度下降算法有效地求解。我们从理论上分析了Simpleemkmkm的性能，以其聚类的概括误差。在11个基准数据集上进行的全面实验表明，Simplemkmkm的表现优于最先进的多内核聚类替代方案。
We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM). It extends the widely used supervised kernel alignment criterion to multi-kernel clustering. Our criterion is given by an intractable minimization-maximization problem in the kernel coefficient and clustering partition matrix. To optimize it, we re-formulate the problem as a smooth minimization one, which can be solved efficiently using a reduced gradient descent algorithm. We theoretically analyze the performance of SimpleMKKM in terms of its clustering generalization error. Comprehensive experiments on 11 benchmark datasets demonstrate that SimpleMKKM outperforms state of the art multi-kernel clustering alternatives.
