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» A New Indexing Method for High Dimensional Dataset
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CVPR
2008
IEEE
14 years 3 months ago
Partitioning of image datasets using discriminative context information
We propose a new method to partition an unlabeled dataset, called Discriminative Context Partitioning (DCP). It is motivated by the idea of splitting the dataset based only on how...
Christoph H. Lampert
BMCBI
2008
117views more  BMCBI 2008»
13 years 8 months ago
New resampling method for evaluating stability of clusters
Background: Hierarchical clustering is a widely applied tool in the analysis of microarray gene expression data. The assessment of cluster stability is a major challenge in cluste...
Irina Gana Dresen, Tanja Boes, Johannes Hüsin...
BMCBI
2007
123views more  BMCBI 2007»
13 years 8 months ago
Robust clustering in high dimensional data using statistical depths
Background: Mean-based clustering algorithms such as bisecting k-means generally lack robustness. Although componentwise median is a more robust alternative, it can be a poor cent...
Yuanyuan Ding, Xin Dang, Hanxiang Peng, Dawn Wilki...
JMLR
2010
119views more  JMLR 2010»
13 years 3 months ago
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
Different aspects of the curse of dimensionality are known to present serious challenges to various machine-learning methods and tasks. This paper explores a new aspect of the dim...
Milos Radovanovic, Alexandros Nanopoulos, Mirjana ...
DEXA
2009
Springer
151views Database» more  DEXA 2009»
14 years 3 months ago
Detecting Projected Outliers in High-Dimensional Data Streams
Abstract. In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector ...
Ji Zhang, Qigang Gao, Hai H. Wang, Qing Liu, Kai X...