119 search results - page 16 / 24 » Sparse kernel methods for high-dimensional survival data |
109
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ECML
2007
Springer
15 years 3 months ago
2007
Springer
Abstract. Principal component analysis (PCA) is a well-known classical data analysis technique. There are a number of algorithms for solving the problem, some scaling better than o...
102
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SCHOLARPEDIA
2008
14 years 7 months ago
2008
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to d...
IVC
2007
14 years 9 months 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...
104
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BMCBI
2011
14 years 4 months ago
2011
Background: Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically all...
DAGM
2010
Springer
14 years 10 months ago
2010
Springer
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...