130 search results - page 2 / 26 » Fuzzy clustering with principal component analysis |
ICML
2004
IEEE
14 years 6 months ago
2004
IEEE
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
CORR
2007
Springer
13 years 6 months ago
2007
Springer
In this paper, we study the application of sparse principal component analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combi...
ECML
2004
Springer
13 years 11 months ago
2004
Springer
This work presents a novel procedure for computing (1) distances between nodes of a weighted, undirected, graph, called the Euclidean Commute Time Distance (ECTD), and (2) a subspa...
ICML
2004
IEEE
14 years 6 months ago
2004
IEEE
Many clustering algorithms fail when dealing with high dimensional data. Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it assumes a ...
NC
2007
13 years 5 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�...