Visualizing regression data by supervised Generative Topographic Mapping
Visualizing regression data by supervised Generative Topographic Mapping
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DOI：
10.1109/scis-isis.2014.7044634
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
Nobuhiko Yamaguchi
中科院分区：
文献类型：
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作者：
Nobuhiko Yamaguchi
生成式地形图（GTM）是Bishop等人提出的一种非线性隐变量模型，是一种数据可视化技术。在本文中，我们提出了监督GTM模型和半监督GTM模型。传统的监督GTM模型在分类问题中使用离散的类标签，因此不能直接处理回归问题中的连续输出标签。为了克服这个问题，我们提出了一个有监督的GTM模型，它可以自然地处理回归问题中的连续输出标签。为了处理丢失的标签，我们还提出了一个半监督的GTM模型，使用标记和未标记的数据。
Generative Topographic Mapping (GTM) is a nonlinear latent variable model introduced by Bishop et al. as a data visualization technique. In this paper, we propose a supervised GTM model and a semi-supervised GTM model. Conventional supervised GTM models use discrete class labels in classification problems, and therefore cannot directly handle continuous output labels in regression problems. To overcome the problem, we propose a supervised GTM model which can naturally handle continuous output labels in regression problems. In order to handle missing labels, we also propose a semi-supervised GTM model that uses both labeled and unlabeled data.