A Discriminative Gaussian Mixture Model with Sparsity
A Discriminative Gaussian Mixture Model with Sparsity
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发表时间：
2019-11
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通讯作者：
Hideaki Hayashi;S. Uchida
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作者：
Hideaki Hayashi;S. Uchida
在概率分类中，基于softmax函数的判别模型具有潜在的局限性，因为它假设特征空间中的每个类都是单峰的。混合模型可以解决这个问题，尽管它会导致参数数量的增加。我们提出了一个稀疏分类器的基础上的歧视GMM，称为稀疏歧视高斯混合（SDGM）。在SDGM中，通过稀疏贝叶斯学习训练基于GMM的判别模型。使用这种稀疏学习框架，我们可以同时去除冗余的高斯分量，并减少学习过程中剩余分量中使用的参数数量;这种学习方法降低了模型的复杂度，从而提高了泛化能力。此外，SDGM可以嵌入到神经网络（NN）中，例如卷积NN，并且可以以端到端的方式进行训练。实验结果表明，该方法优于现有的基于softmax的判别模型。
In probabilistic classification, a discriminative model based on the softmax function has a potential limitation in that it assumes unimodality for each class in the feature space. The mixture model can address this issue, although it leads to an increase in the number of parameters. We propose a sparse classifier based on a discriminative GMM, referred to as a sparse discriminative Gaussian mixture (SDGM). In the SDGM, a GMM-based discriminative model is trained via sparse Bayesian learning. Using this sparse learning framework, we can simultaneously remove redundant Gaussian components and reduce the number of parameters used in the remaining components during learning; this learning method reduces the model complexity, thereby improving the generalization capability. Furthermore, the SDGM can be embedded into neural networks (NNs), such as convolutional NNs, and can be trained in an end-to-end manner. Experimental results demonstrated that the proposed method outperformed the existing softmax-based discriminative models.