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Improving classification with latent variable models by sequential constraint optimization
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DOI：10.1016/j.neucom.2003.10.001.png)
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摘要
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In this paper we propose a method to use multiple generative models with latent variables for classification tasks. The standard approach to use generative models for classification is to train a separate model for each class. A novel data point is then classified by the model that attributes the highest probability. The algorithm we propose modifies the parameters of the models to improve the classification accuracy. Our approach is made computationally tractable by assuming that each of the models is deterministic, by which we mean that a data-point is associated to only a single latent state. The resulting algorithm is a variant of the support vector machine learning algorithm and in a limiting case the method is similar to the standard perceptron learning algorithm. We apply the method to two types of latent variable models. The first has a discrete latent state space and the second, principal component analysis, has a continuous latent state space. We compare the effectiveness of both approaches on a handwritten digit recognition problem and on a satellite image recognition problem. (C) 2003 Elsevier B.V. All rights reserved.
Keyword:
latent variable models
semi-supervised learning
support vector machines
PCA
vector quantization
image and character recognition
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期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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