论文标题
多个跨媒体聚类的扁平预测
Multiple Flat Projections for Cross-manifold Clustering
论文作者
论文摘要
跨界聚类是一个困难的话题，许多传统的聚类方法因跨曼佛结构而失败。在本文中，我们提出了一个多个平面预测聚类（MFPC）来处理跨曼群聚类问题。在我们的MFPC中，给定样品被投影到多个子空间中，以发现隐式歧管的全局结构。因此，跨曼群簇与各种投影区别开来。此外，我们的MFPC通过内核技巧扩展到非线性歧管聚类，以处理更复杂的跨曼群聚类。 MFPC中的一系列非凸矩阵优化问题通过提出的递归算法解决。合成测试表明，我们的MFPC很好地在跨策略结构上工作。此外，与某些最先进的聚类方法相比，基准数据集的实验结果表明，我们的MFPC的表现出色。
Cross-manifold clustering is a hard topic and many traditional clustering methods fail because of the cross-manifold structures. In this paper, we propose a Multiple Flat Projections Clustering (MFPC) to deal with cross-manifold clustering problems. In our MFPC, the given samples are projected into multiple subspaces to discover the global structures of the implicit manifolds. Thus, the cross-manifold clusters are distinguished from the various projections. Further, our MFPC is extended to nonlinear manifold clustering via kernel tricks to deal with more complex cross-manifold clustering. A series of non-convex matrix optimization problems in MFPC are solved by a proposed recursive algorithm. The synthetic tests show that our MFPC works on the cross-manifold structures well. Moreover, experimental results on the benchmark datasets show the excellent performance of our MFPC compared with some state-of-the-art clustering methods.
