Relational Neural Gas
Relational Neural Gas
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DOI：
10.1007/978-3-540-74565-5_16
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发表时间：
2007-09
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影响因子：
--
通讯作者：
B. Hammer;Alexander Hasenfuss
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文献类型：
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作者：
B. Hammer;Alexander Hasenfuss
我们引入了神经气体的关系变体，这是一种非常高效和强大的神经聚类算法，它允许对根据两两相似或不相似矩阵给出的数据进行聚类和挖掘。假设该矩阵分别来源于欧几里得距离或点积，然而，点的底层嵌入是未知的。可以根据给定的相似性或差异性等效地制定批处理优化，从而提供一种将批处理优化转移到关系数据的方法。该过程保证了收敛性，并且可以很容易地将标签信息的集成等扩展转移到该框架中。
We introduce relational variants of neural gas, a very efficient and powerful neural clustering algorithm, which allow a clustering and mining of data given in terms of a pairwise similarity or dissimilarity matrix. It is assumed that this matrix stems from Euclidean distance or dot product, respectively, however, the underlying embedding of points is unknown. One can equivalently formulate batch optimization in terms of the given similarities or dissimilarities, thus providing a way to transfer batch optimization to relational data. For this procedure, convergence is guaranteed and extensions such as the integration of label information can readily be transferred to this framework.