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ICIP
2005
IEEE
14 years 11 months ago
Largest-eigenvalue-theory for incremental principal component analysis
In this paper, we present a novel algorithm for incremental principal component analysis. Based on the LargestEigenvalue-Theory, i.e. the eigenvector associated with the largest ei...
Shuicheng Yan, Xiaoou Tang
ISQED
2000
IEEE
117views Hardware» more  ISQED 2000»
14 years 2 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
NIPS
2008
13 years 11 months ago
Kernel Measures of Independence for non-iid Data
Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion. In this paper, we extend this c...
Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smo...
AVBPA
2001
Springer
134views Biometrics» more  AVBPA 2001»
14 years 1 months ago
Pose-Independent Face Identification from Video Sequences
A scheme for pose-independent face recognition is presented. An "unwrapped" texture map is constructed from a video sequence using a texture-from-motion approach, which ...
Michael C. Lincoln, Adrian F. Clark
ASPDAC
2009
ACM
164views Hardware» more  ASPDAC 2009»
14 years 4 months ago
Accounting for non-linear dependence using function driven component analysis
Majority of practical multivariate statistical analyses and optimizations model interdependence among random variables in terms of the linear correlation among them. Though linear...
Lerong Cheng, Puneet Gupta, Lei He