24 search results - page 3 / 5 » Constrained Clustering by Spectral Kernel Learning |
ICML
2005
IEEE
14 years 9 months ago
2005
IEEE
Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are...
DAGM
2011
Springer
12 years 8 months ago
2011
Springer
Many unsupervised learning algorithms make use of kernels that rely on the Euclidean distance between two samples. However, the Euclidean distance is optimal for Gaussian distribut...
ICML
2004
IEEE
14 years 9 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
2010
Springer
13 years 6 months ago
2010
Springer
Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors a...
KDD
2005
ACM
14 years 9 months ago
2005
ACM
Graph clustering (also called graph partitioning) -- clustering the nodes of a graph -- is an important problem in diverse data mining applications. Traditional approaches involve...