This paper presents a polynomial algorithm for learning mixtures of logconcave distributions in R in the presence of malicious noise. That is, each sample is corrupted with some small probability, being replaced by a point about which we can make no assumptions. A key element of the algorithm is Robust Principle Components Analysis (PCA), which is less susceptible to corruption by noisy points....

1. student research committee, urmia university of medical sciences, urmia, iran 2. dept. of medical physics, faculty of medicine, urmia university of medical sciences, urmia, iran 3. dept. of radiology, faculty of medicine, imam khomeini hospital, urmia university of medical sciences, urmia, iran                             corresponding author: akbar gharbali, phd; assistant professor of medi...

Abstract This paper proposes a method for diagnosing bolt looseness faults using the principle of PCA to extract time-domain features monitoring data. First all, five dimensionless factors IMF are calculated after empirical mode decomposition (EMD) is performed on original Then, principal component analysis (PCA) applied data vectors, which processed by dimensionality reduction and residual spa...

Supervised dimensionality reduction has shown great advantages in finding predictive subspaces. Previous methods rarely consider the popular maximum margin principle and are prone to overfitting to usually small training data, especially for those under the maximum likelihood framework. In this paper, we present a posterior-regularized Bayesian approach to combine Principal Component Analysis (...

The set-point tracking of certain process variable trajectories is often needed for the lower level control in batch processes so as to achieve desirable final product quality for the higher level control. In order to realize trajectory tracking successfully, process models should be known in advance. In fact, process models play an essential role in trajectory tracking. Due to the difficulty f...

This paper studies the application of principal component analysis, multiple polynomial regression, and artificial neural network ANN techniques to the quantitative analysis of binary mixture of dye solution. The binary mixtures of three textile dyes including blue, red and yellow colors were analyzed by PCA-Multiple polynomial Regression and PCA-Artificial Neural network PCA-ANN methods. The o...

Principle Component Analysis (PCA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in the traditional PCA some weaknesses. In this paper, we propose new PCA-based methods that can improve the performance of the traditional PCA and two-dimensional PCA (2DPCA) approach...