Projection pursuit mixture density estimation
Projection pursuit mixture density estimation
复制标题
DOI：
10.1109/tsp.2005.857007
复制
发表时间：
2005-11-01
影响因子：
5.4
通讯作者：
Aladjem, M
中科院分区：
文献类型：
--
作者：
Aladjem, M
本文寻求一个n变量概率密度函数的高斯混合模型（GMM）。通常，GMMs的参数是通过优化最大似然准则在原始n维空间中确定的。这种拟合GMMs方法的一个实际缺陷是在处理高维数据时性能不佳，因为需要大样本量来匹配低维数据的精度。提出了一种基于投影寻迹策略的GMM拟合方法。与GMM在高维上的直接NIL拟合相比，这种GMM是高度受限的，因此它在子空间中建模结构的能力得到了增强。我们的方法与最近发展的独立因子分析（IFA）混合模型密切相关。与n维和IFA混合GMM的NIL拟合的比较表明，该方法对于使用小尺寸训练集拟合GMM是一种有吸引力的选择。
In this paper we seek a Gaussian mixture model (GMM) of an n-variate probability density function. Usually the parameters of GMMs are determined in the original n-dimensional space by optimizing a maximum likelihood (ML) criterion. A practical deficiency of this method of fitting GMMs is its poor performance when dealing with high-dimensional data since a large sample size is needed to match the accuracy that is possible in low dimensions. We propose a method for fitting the GMM based on the projection pursuit strategy. This GMM is highly constrained and hence its ability to model structure in subspaces is enhanced, compared to a direct NIL fitting of a GMM in high dimensions. Our method is closely related to recently developed independent factor analysis (IFA) mixture models. The comparisons with NIL fitting of GMM in n-dimensions and IFA mixtures show that the proposed method is an attractive choice for fitting GMMs using small sizes of training sets.