A novel non-linear modifier for adaptive illumination normalization for robust face recognition
In this paper, a novel approach is presented for adaptive illumination normalization for face
recognition under varying illuminations due to change in angle of light projection …
recognition under varying illuminations due to change in angle of light projection …
Mixture semisupervised principal component regression model and soft sensor application
Traditionally, data‐based soft sensors are constructed upon the labeled historical dataset
which contains equal numbers of input and output data samples. While it is easy to obtain …
which contains equal numbers of input and output data samples. While it is easy to obtain …
Linear discriminant regression classification for face recognition
SM Huang, JF Yang - IEEE signal processing letters, 2012 - ieeexplore.ieee.org
To improve the robustness of the linear regression classification (LRC) algorithm, in this
paper, we propose a linear discriminant regression classification (LDRC) algorithm to boost …
paper, we propose a linear discriminant regression classification (LDRC) algorithm to boost …
Superimposed sparse parameter classifiers for face recognition
In this paper, a novel classifier, called superimposed sparse parameter (SSP) classifier is
proposed for face recognition. SSP is motivated by two phase test sample sparse …
proposed for face recognition. SSP is motivated by two phase test sample sparse …
Face recognition based on PCA and logistic regression analysis
Face recognition is an important research hotspot. More and more new methods have been
proposed in recent years. In this paper, we propose a novel face recognition method which …
proposed in recent years. In this paper, we propose a novel face recognition method which …
Mixture semisupervised probabilistic principal component regression model with missing inputs
Principal component regression (PCR) has been widely used as a multivariate method for
data-based soft sensor design. In order to take advantage of probabilistic features, it has …
data-based soft sensor design. In order to take advantage of probabilistic features, it has …
Kernel ridge regression classification
J He, L Ding, L Jiang, L Ma - 2014 International Joint …, 2014 - ieeexplore.ieee.org
We present a nearest nonlinear subspace classifier that extends ridge regression
classification method to kernel version which is called Kernel Ridge Regression …
classification method to kernel version which is called Kernel Ridge Regression …
Nonlinear semisupervised principal component regression for soft sensor modeling and its mixture form
Compared with daily recorded process variables that can be easily obtained through the
distributed control system, acquirements of key quality variables are much more difficult. As …
distributed control system, acquirements of key quality variables are much more difficult. As …
A novel approach of face recognition using optimized adaptive illumination–normalization and KELM
Light variations from different directions on the face images cause severe performance
degradation in face recognition system. These variations should be nullified or suppressed …
degradation in face recognition system. These variations should be nullified or suppressed …
Comprehensive economic index prediction based operating optimality assessment and nonoptimal cause identification for multimode processes
Y Liu, F Wang, Y Chang, R Ma - Chemical Engineering Research and …, 2015 - Elsevier
For many multimode processes, the process operating performance may deteriorate with
time from optimal state due to process disturbances, noise, and other uncertainties, and it is …
time from optimal state due to process disturbances, noise, and other uncertainties, and it is …