0


什么是集群:博弈论视角

What Is a Cluster: Perspectives from Game Theory
课程网址: http://videolectures.net/nipsworkshops09_pelillo_cpg/  
主讲教师: Marcello Pelillo
开课单位: 加州大学
开课时间: 2010-01-19
课程语种: 英语
中文简介:
而不是坚持确定输入数据的分区,并因此获得作为分区过程的副产品的集群的想法,在这个演示中,我建议颠倒问题的条款,并试图得出这个概念的严格表述集群很明显,概念性的问题“什么是集群?”在其完全的普遍性中是无望的,它的伴随“什么是最优的聚类?”在过去的几十年里一直占据着文学的主导地位,两者都是同一个问题的两个方面。然而,试图回答前一个问题,除了为聚类问题的本质提供新的亮点外,还可以让我们自然地克服上面提到的分区方法的主要局限,并处理更一般的问题。问题,例如,群集可能重叠,杂乱的元素可能会被取消分配,从而有望减少理论与实践之间的差距。在我们努力回答上面提出的问题时,我们发现游戏理论提供了一个非常优雅和一般的视角。我们的目的。因此,在演示文稿的第二部分,建设性部分,我将描述一个用于聚类的游戏理论框架[21,25,22],该框架已经在计算机视觉和生物信息学等领域中得到应用。起点是基本观察“簇”可以被非正式地定义为非常相干的数据项集,即,作为输入数据C的子集,其满足内部标准(属于C的所有元素应该彼此高度相似)和外部(不大)集群应该包含C作为适当的子集)。然后我们将聚类问题表述为非合作聚类游戏。在这种情况下,群集的概念与(进化)博弈论中的经典均衡概念等效,后者反映了上述内部和外部群集条件。
课程简介: Instead of insisting on the idea of determining a partition of the input data, and hence obtaining the clusters as a by-product of the partitioning process, in this presentation I propose to reverse the terms of the problem and attempt instead to derive a rigorous formulation of the very notion of a cluster. Clearly, the conceptual question “what is a cluster?” is as hopeless, in its full generality, as is its companion “what is an optimal clustering?” which has dominated the literature in the past few decades, both being two sides of the same coin. An attempt to answer the former question, however, besides shedding fresh light into the nature of the clustering problem, would allow us, as a consequence, to naturally overcome the major limitations of the partitional approach alluded to above, and to deal with more general problems where, e.g., clusters may overlap and clutter elements may get unassigned, thereby hopefully reducing the gap between theory and practice. In our endeavor to provide an answer to the question raised above, we found that game theory offers a very elegant and general perspective that serves well our purposes. Hence, in the second, constructive part of the presentation I will describe a game-theoretic framework for clustering [21, 25, 22] which has found applications in fields as diverse as computer vision and bioinformatics. The starting point is the elementary observation that a “cluster” may be informally defined as a maximally coherent set of data items, i.e., as a subset of the input data C which satisfies both an internal criterion (all elements belonging to C should be highly similar to each other) and an external one (no larger cluster should contain C as a proper subset). We then formulate the clustering problem as a non-cooperative clustering game. Within this context, the notion of a cluster turns out to be equivalent to a classical equilibrium concept from (evolutionary) game theory, as the latter reflects both the internal and external cluster conditions mentioned above.
关 键 词: 集群; 游戏理论框架; 计算机视觉
课程来源: 视频讲座网
最后编审: 2019-09-07:lxf
阅读次数: 65