A pooled subspace mixture density model for pattern classification in high-dimensional spaces

Density estimation in high-dimensional data spaces is a challenge due to the sparseness of data which is known as ldquothe curse of dimensionalityrdquo. Researchers often resort to low-dimensional subspaces for such tasks, while discard the distribution in the complementary subspace. In this paper, we propose a new mixture density model based on pooled subspace. In our method, the Gaussian components of each class share a subspace and the complementary subspace is incorporated in the density function. The subspace and Gaussian mixture density are estimated simultaneously in EM iteration steps. We apply the density model to pattern classification in experiments on UCI datasets and compare the proposed method with previous ones. The experimental results demonstrate the superiority of the proposed method.