.png){kind=icon}
返回
Random direction divisive clustering
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2012.09.008.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Projection methods for dimension reduction have enabled the discovery of otherwise unattainable structure in ultra high dimensional data. More recently, a particular method, namely Random Projection, has been shown to have the advantage of high quality data representations with minimal computation effort, even for data dimensions in the range of hundreds of thousands or even millions. Here, we couple this dimension reduction technique with data clustering algorithms that are specially designed for high dimensional cases. First, we show that the theoretical properties of both components can be combined in a sound manner, promising an effective clustering framework. Indeed, for a series of simulated and real ultra high dimensional data scenarios, as the experimental analysis shows, the resulting algorithms achieve high quality data partitions, orders of magnitude faster. (C) 2012 Elsevier B.V. All rights reserved.
Keyword:
Clustering
Principal Component Analysis
Random Projection
Kernel Density Estimation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
.png){kind=icon}
分享收藏Normative pediatric visual acuity using single surrounded HOTV optotypes on the Electronic Visual Acuity Tester following the Amblyopia Treatment Study protocol根据弱视治疗研究方案，在电子视力测试仪上使用单个包围的HOTV视标的儿童视力
OAAI
OAAI分享收藏分享收藏Pipe Burst Detection, Localization, and Quantification Using the Transient Pressure Damping Method
PREAI
分享收藏
查看更多内容