119 search results - page 16 / 24 » Sparse kernel methods for high-dimensional survival data |
ECML
2007
Springer
14 years 2 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...
SCHOLARPEDIA
2008
13 years 6 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
13 years 8 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...
BMCBI
2011
13 years 3 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
13 years 9 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...