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2010
Springer
14 years 8 months ago
Extracting Principal Components from Pseudo-random Data by Using Random Matrix Theory
We develop a methodology to grasp temporal trend in a stock market that changes year to year, or sometimes within a year depending on numerous factors. For this purpose, we employ ...
Mieko Tanaka-Yamawaki
ICIP
2008
IEEE
15 years 11 months ago
Principal Component Analysis of spectral coefficients for mesh watermarking
This paper proposes a new robust 3-D object blind watermarking method using constraints in the spectral domain. Mesh watermarking in spectral domain has the property of spreading ...
Ming Luo, Adrian G. Bors
ASPDAC
2009
ACM
164views Hardware» more  ASPDAC 2009»
15 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
ECML
2007
Springer
15 years 4 months ago
Principal Component Analysis for Large Scale Problems with Lots of Missing Values
Abstract. Principal component analysis (PCA) is a well-known classical data analysis technique. There are a number of algorithms for solving the problem, some scaling better than o...
Tapani Raiko, Alexander Ilin, Juha Karhunen
ISSRE
2003
IEEE
15 years 3 months ago
Assessing Uncertainty in Reliability of Component-Based Software Systems
Many architecture–based software reliability models were proposed in the past. Regardless of the accuracy of these models, if a considerable uncertainty exists in the estimates ...
Katerina Goseva-Popstojanova, Sunil Kamavaram