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ISNN
2004
Springer
14 years 1 months ago
Progressive Principal Component Analysis
Abstract. Principal Component Analysis (PCA) is a feature extraction approach directly based on a whole vector pattern and acquires a set of projections that can realize the best r...
Jun Liu, Songcan Chen, Zhi-Hua Zhou
FPGA
2006
ACM
156views FPGA» more  FPGA 2006»
13 years 11 months ago
A reconfigurable architecture for network intrusion detection using principal component analysis
In this paper, we develop an architecture for principal component analysis (PCA) to be used as an outlier detection method for high-speed network intrusion detection systems (NIDS...
David T. Nguyen, Gokhan Memik, Alok N. Choudhary
JMLR
2010
163views more  JMLR 2010»
13 years 2 months ago
Dense Message Passing for Sparse Principal Component Analysis
We describe a novel inference algorithm for sparse Bayesian PCA with a zero-norm prior on the model parameters. Bayesian inference is very challenging in probabilistic models of t...
Kevin Sharp, Magnus Rattray
IPAS
2010
13 years 5 months ago
An unsupervised learning approach for facial expression recognition using semi-definite programming and generalized principal co
In this paper, we consider facial expression recognition using an unsupervised learning framework. Specifically, given a data set composed of a number of facial images of the same...
Behnood Gholami, Wassim M. Haddad, Allen Tannenbau...
ICPR
2006
IEEE
14 years 9 months ago
Multilinear Principal Component Analysis of Tensor Objects for Recognition
In this paper, a multilinear formulation of the popular Principal Component Analysis (PCA) is proposed, named as multilinear PCA (MPCA), where the input can be not only vectors, b...
Anastasios N. Venetsanopoulos, Haiping Lu, Konstan...