GTM: The generative topographic mapping
GTM: The generative topographic mapping
复制标题
DOI：
10.1162/089976698300017953
复制
发表时间：
1998-01-01
影响因子：
2.9
通讯作者：
Williams, CKI
中科院分区：
文献类型：
--
作者：
Bishop, CM;Svensen, M;Williams, CKI
潜变量模型用较少数量的潜变量来表示数据在多维空间中的概率密度。一个熟悉的例子是因子分析，它基于潜在空间和数据空间之间的线性变换。本文介绍了一种基于产生式地形映射的非线性潜变量模型，该模型的参数可以用期望最大化算法来确定。GTM提供了Kohonen(1982)广泛使用的自组织映射(SOM)的一种原则性替代方案，并克服了SOM的大多数重要限制。我们展示了GTM算法在玩具问题和多相输油管道诊断中的模拟数据上的性能。
Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis, which is based on a linear transformation between the latent space and the data space. In this article, we introduce a form of nonlinear latent variable model tailed the generative topographic mapping, for which the parameters of the model can be determined using the expectation-maximization algorithm. GTM provides a principled alternative to the widely used self-organizing map (SOM) of Kohonen (1982) and overcomes most of the significant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from now diagnostics for a multiphase oil pipeline.