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Principal component analysis

Known as: Principle components analysis, Principle Component Analysis, Probabilistic principal component analysis 
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly… 
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2010
2010
This paper presents a simple graphic method for detecting and classifying faults in point mechanisms based on the study of some… 
Highly Cited
2010
Highly Cited
2010
In this paper, we present a graph based face representation for efficient age invariant face recognition. The graph contains… 
2009
2009
Anomaly detection algorithms applied to hyperspectral imagery are able to reliably identify man-made objects from a natural… 
2006
2006
Stochastic dynamic programming models are attractive for multireservoir control problems because they allow non‐linear features… 
Highly Cited
2005
Highly Cited
2005
A principal challenge in the use of empirical proper orthogonal decomposition (POD) Galerkin models for feedback control design… 
Highly Cited
1999
Highly Cited
1999
We present a face detection algorithm for color images with complex background. We include color information into a face… 
1998
1998
Research has been initiated to determine a set of six basis colorants which are the best representation of artwork such as… 
1996
1996
  • M. Covell
  • 1996
  • Corpus ID: 9057102
Eigen-points estimates the image-plane locations of fiduciary points on an object. By estimating multiple locations… 
Highly Cited
1994
Highly Cited
1994
This study looks at the relative importance of the factors which control the concentration of atmospheric carbon dioxide. EOF… 
Highly Cited
1986
Highly Cited
1986
The concept of fast KL transform coding introduced earlier [7], [8] for first-order Markov processes and certain random fields…