With our previous study, the Super-k algorithm, we have introduced a novel way of piecewise-linear classification. While working on the Super-k algorithm, we have found that there is a similar, and simpler way to explain for obtaining a piecewise-linear classifier based on Voronoi tessellations. Replacing the multidimensional voxelization and expectation-maximization stages of the algorithm with a distance-based clustering algorithm, preferably k-means, works as well as the prior approach. Since we are replacing the voxelization with the clustering, we have found it meaningful to name the modified algorithm, with respect to Super-k, as Supervised k Clusters or in short Super-klust. Similar to the Super-k algorithm, the Super-klust algorithm covers data with a labeled Voronoi tessellation, and uses resulting tessellation for classification. According to the experimental results, the Super-klust algorithm has similar performance characteristics with the Super-k algorithm.
翻译:以我们先前的研究,即超K算法,我们引入了一种新颖的片段线性分类方法。在使用超K算法时,我们发现有一个相似的、更简单的解释方法来根据Voronoi 星系变相法获得一个小片线性分类器。用远程组合算法取代该算法的多维倍化和预期-最大化阶段,最好是K-运算法,工作方式和先前的方法。由于我们正在用集束取代异化法,我们发现将修改的算法命名为超K,作为超级K类或短超KLust。类似于超K算法,超级Klust算法包含数据,标注为Voronooi 星系,并使用由此得出的星系分类法。根据实验结果,超K值算法与超级K算法具有相似的性能特性。