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» HITS is Principal Components Analysis
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ECML
2004
Springer
15 years 9 months ago
The Principal Components Analysis of a Graph, and Its Relationships to Spectral Clustering
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...
Marco Saerens, François Fouss, Luh Yen, Pie...
ICPR
2000
IEEE
16 years 5 months ago
Sign of Gaussian Curvature from Eigen Plane Using Principal Components Analysis
This paper describes a new method to recover the sign of the local Gaussian curvature at each point on the visible surface of a 3-D object. Multiple (p > 3) shaded images are a...
Shinji Fukui, Yuji Iwahori, Akira Iwata, Robert J....
ICML
2004
IEEE
16 years 4 months ago
K-means clustering via principal component analysis
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...
Chris H. Q. Ding, Xiaofeng He
CORR
2007
Springer
167views Education» more  CORR 2007»
15 years 4 months ago
Optimal Solutions for Sparse Principal Component Analysis
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonze...
Alexandre d'Aspremont, Francis R. Bach, Laurent El...
ISQED
2000
IEEE
117views Hardware» more  ISQED 2000»
15 years 8 months ago
Realistic Worst-Case Modeling by Performance Level Principal Component Analysis
A new algorithm to determine the number and value of realistic worst-case models for the performance of module library components is presented in this paper. The proposed algorith...
Alessandra Nardi, Andrea Neviani, Carlo Guardiani