Variational Inference of Infinite Generalized Gaussian Mixture Models with Feature Selection

Srikanth Amudala, Samr Ali, N. Bouguila
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引用次数: 1

Abstract

This paper presents a variational learning framework for the infinite generalized Gaussian mixture (IGGM) model. The generalized Gaussian distribution (GGD) has a proven capability in modeling complex multidimensional data due to the flexibility of its shape parameter. Infinite model addresses the model selection problem; i.e., determination of the number of clusters without recourse to the classical selection criteria such that the number of mixture components increases automatically to best model available data accordingly. We also incorporate feature selection to consider the features that are most appropriate in constructing an approximate model in terms of clustering accuracy. Experimental results on a medical application and image categorization show the effectiveness of the proposed algorithm.
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具有特征选择的无限广义高斯混合模型的变分推理
本文提出了无限广义高斯混合模型的变分学习框架。广义高斯分布(GGD)由于其形状参数的灵活性,在复杂多维数据建模中具有良好的应用前景。无限模型解决了模型选择问题;即,在不依赖经典选择标准的情况下确定聚类的数量,从而使混合成分的数量自动增加到相应的最佳模型可用数据。我们还结合了特征选择,以考虑在聚类精度方面构建近似模型时最合适的特征。在医学应用和图像分类方面的实验结果表明了该算法的有效性。
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