adaptive dimension reduction for clustering high dimensional data

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1. Introduction

Clustering methods such as the K-means and EM suffer from local minima problems. In high dimensional space, the cost function surface is very rugged and it is easy to get trapped somewhere close to the initial configurations.

2. Effective Dimension for Clustering

3. EM in relevant subspace

4. Adaptive Dimension Reduction for EM

5. Adaptive Dimension Reduction for K-means