一种基于ICA空间高斯混合模型的新颖检测*

新颖检测中，可应用高斯混合模型建立已知数据模型，拟合数据分布，但当数据维数较高时，自由参数太多，训练需要巨大的数据采样，而ICA搜寻数据的最大统计独立表示，可以将数据从高维空间投影到低维空间。提出一种基于ICA空间高斯混合模型的新颖检测，可有效减少估测的自由参数，降低训练数据采样的苛刻要求，实验也验证了该方法的可行性。

Novelty detection based on Gaussian mixture models in ICA space

A novelty detector learns the model of normality in the training stage using only normal samples and abnormalities are then identified by testing for novelty against that model. Gaussian mixture models can be used to model data general distributions for novelty detection. But given high data dimensionality, a very large number of training samples are needed for modeling, there are also too many free parameters. ICA is a subspace projection technique that can project data from a high-dimensional space to a lower-dimensional space by computing independent components of the data. So this paper proposed a novelty detection based on Gaussian mixture models in ICA space, which could improve the dimension curse problem and decrease the free parameters. The method is verified by the experiments.