298 search results - page 34 / 60 » Subspace Clustering of High Dimensional Data |
76
Voted
KDD
2003
ACM
16 years 26 days ago
2003
ACM
High dimensional directional data is becoming increasingly important in contemporary applications such as analysis of text and gene-expression data. A natural model for multivaria...
APBC
2004
15 years 1 months ago
2004
Using gene expression data for cancer detection is one of the famous research topics in bioinformatics. Theoretically, gene expression data is capable to detect all types of early...
106
click to vote
IDEAL
2004
Springer
15 years 5 months ago
2004
Springer
Microarray datasets are often too large to visualise due to the high dimensionality. The self-organising map has been found useful to analyse massive complex datasets. It can be us...
95
Voted
SDM
2003
SIAM
15 years 1 months ago
2003
SIAM
A major challenge in document clustering is the extremely high dimensionality. For example, the vocabulary for a document set can easily be thousands of words. On the other hand, ...
108
click to vote
IVC
2007
15 years 11 days ago
2007
- Canonical correlation analysis (CCA) is a major linear subspace approach to dimensionality reduction and has been applied to image processing, pose estimation and other fields. H...