The recent paper (Cheung 2001) has studied the blind identification of Gaussian source process through a general temporal independent component analysis (ICA) approach named dual autoregressive modelling. It is actually a temporal extension of the classical principal component analysis without considering the principal order of the components. In this paper, we will further show the identifiabl...

The method of sparse principal component analysis (S-PCA) proposed by Zou, Hastie, and Tibshirani (2006) is an attractive approach to obtain sparse loadings in principal component analysis (PCA). S-PCA was motivated by reformulating PCA as a least-squares problem so that a lasso penalty on the loading coefficients can be applied. In this article, we propose new estimates to improve S-PCA in the...

To assess the learning of word form, pronunciation, and meaning in an unfamiliar writing system, we carried out Event Related Potential (ERP) experiments with learners of Chinese at the end of their first and second terms of Chinese class at an American university. The subjects were required to recognize a target Chinese character or English word with ERP recorded. They named filler targets ind...

This paper presents a comparative analysis of a new unsupervised PCA-based technique for steel plates texture segmentation towards defect detection. The proposed scheme called Variance Based Component Analysis or VBCA employs PCA for feature extraction, applies a feature reduction algorithm based on variance of eigenpictures and classifies the pixels as defective and normal. While the classic P...

A Distributed Denial of Service (DDoS) attack is a major threat to cyber security. It originates from the network layer or the application layer of compromised/attacker systems which are connected to the network. The impact of this attack ranges from the simple inconvenience to use a particular service to causing major failures at the targeted server. When there is heavy traffic flow to a targe...

Principal component analysis (PCA) is a popular tool in multivariate statistics and pattern recognition. Recently, some mixture models of local principal component analysis have attracted attention due to a number of bene"ts over global PCA. In this paper, we propose a mixture model by concurrently performing global data partition and local linear PCA. The partition is optimal or near optimal, ...

Genome-wide pathway association studies provide novel insight into the biological mechanism underlying complex diseases. Current pathway association studies primarily focus on single important disease phenotype, which is sometimes insufficient to characterize the clinical manifestations of complex diseases. We present a multi-phenotypes pathway association study(MPPAS) approach using principle ...

Face recognition is an important part of our daily life. Face recognition is used either for verification (one-to-one matching) or for identification (one-to-many). Mainly face recognition consists of two categories feature based and appearance based. Feature-based method first process the input image to identify and measure distinctive facial features such as the eyes, mouth, nose, etc., as we...

Machine Learning (ML) approaches have a great impact in fault prediction. Demand for producing quality assured software in an organization has been rapidly increased during the last few years. This leads to increase in development of machine learning algorithms for analyzing and classifying the data sets, which can be used in constructing models for predicting the important quality attributes s...

The exponential rise in the demand of vision based traffic surveillance systems have motivated academia-industries to develop optimal vehicle detection and classification scheme. In this paper, an adaptive learning rate based Gaussian mixture model (GMM) algorithm has been developed for background subtraction of multilane traffic data. Here, vehicle rear information and road dash-markings have ...