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» Nonlinear principal component analysis of noisy data
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CSDA
2008
80views more  CSDA 2008»
13 years 7 months ago
Variational Bayesian functional PCA
A Bayesian approach to analyze the modes of variation in a set of curves is suggested. It is based on a generative model thus allowing for noisy and sparse observations of curves....
Angelika van der Linde
JACM
2011
152views more  JACM 2011»
12 years 10 months ago
Robust principal component analysis?
This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component i...
Emmanuel J. Candès, Xiaodong Li, Yi Ma, Joh...
ICRA
1998
IEEE
148views Robotics» more  ICRA 1998»
13 years 12 months ago
Position Estimation Using Principal Components of Range Data
1 sensors is to construct a structural description from sensor data and to match this description to a previously acquired model [Crowley 85]. An alternative is to project individu...
James L. Crowley, Frank Wallner, Bernt Schiele
JMLR
2006
138views more  JMLR 2006»
13 years 7 months ago
Noisy-OR Component Analysis and its Application to Link Analysis
We develop a new component analysis framework, the Noisy-Or Component Analyzer (NOCA), that targets high-dimensional binary data. NOCA is a probabilistic latent variable model tha...
Tomás Singliar, Milos Hauskrecht
CVPR
2006
IEEE
14 years 9 months ago
Selecting Principal Components in a Two-Stage LDA Algorithm
Linear Discriminant Analysis (LDA) is a well-known and important tool in pattern recognition with potential applications in many areas of research. The most famous and used formul...
Aleix M. Martínez, Manli Zhu