# Difference between revisions of "adaptive dimension reduction for clustering high dimensional data"

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== 1. Introduction == | == 1. Introduction == | ||

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+ | 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 == | == 2. Effective Dimension for Clustering == |

## Revision as of 19:52, 21 July 2013

## Contents

## 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.