502 search results - page 18 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
KDD
2006
ACM
14 years 8 months ago
2006
ACM
Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When label...
NC
2007
13 years 7 months ago
2007
Sorting of the extracellularly recorded spikes is a basic prerequisite for analysis of the cooperative neural behavior and neural code. Fundamentally the sorting performance is de�...
BMCBI
2011
13 years 2 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...
ICDM
2006
IEEE
14 years 1 months ago
2006
IEEE
Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples ar...
ISNN
2004
Springer
14 years 28 days ago
2004
Springer
Abstract. Principal Component Analysis (PCA) is a feature extraction approach directly based on a whole vector pattern and acquires a set of projections that can realize the best r...