119 search results - page 8 / 24 » Sparse kernel methods for high-dimensional survival data |
ICASSP
2010
IEEE
13 years 8 months ago
2010
IEEE
Recently, the Sparse Matrix Transform (SMT) has been proposed as a tool for estimating the eigen-decomposition of high dimensional data vectors [1]. The SMT approach has two major...
KDD
2008
ACM
14 years 8 months ago
2008
ACM
Document classification presents difficult challenges due to the sparsity and the high dimensionality of text data, and to the complex semantics of the natural language. The tradi...
NIPS
2000
13 years 9 months ago
2000
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...
ICCV
2011
IEEE
12 years 8 months ago
2011
IEEE
Linear subspace learning (LSL) is a popular approach to image recognition and it aims to reveal the essential features of high dimensional data, e.g., facial images, in a lower di...
PR
2008
13 years 8 months ago
2008
7 Linear discriminant analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled p...