With increasing complexity in electronic systems there is a need for system level anomaly detection and fault isolation. Anomaly detection based on vector similarity to a training set is used in this paper through two approaches, one the preserves the original information, Mahalanobis Distance (MD), and the other that compresses the data into its principal components, Projection Pursuit Analysi...

Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon's information-theoretic approach and the projection pursuit approach. Using maximum entropy approximations ...

The method of partial least-squares regression (PLSR) can effectively deal with the problems of multicollinearity among independent variables, but can not ideally solve the complicated problems of nonlinearity between dependent variables and independent variables. The method of coupling model with back propagations artificial neural network (BP-ANN) and projection pursuit (PP) is an ideal tool ...

Principal subspace theorems deal with the problem of finding subspaces supporting optimal approximations of multivariate distributions. The optimality criterion considered in this paper is the minimization of the mean squared distance between the given distribution and an approximating distribution, subject to some constraints. Statistical applications include, but are not limited to, cluster a...

Detecting outliers in the context of multivariate data is known as an important but difficult task and there already exist several detection methods. Most of the proposed methods are based either on the Mahalanobis distance of the observations to the center of the distribution or on a projection pursuit (PP) approach. In the present paper we focus on the one-dimensional PP approach which may be...

The theory and application of time-series clustering analysis is an effective explanatory technique in various research fields. To overcome the limitations and many assumptions in conventional model-based clustering, this study utilizes the projection pursuit regression method as explanatory tool for formulating, identifying and estimating nonlinear models to approach the complex regression sur...

Gold price has significant nonlinearity and time-variance with many indeterminate influencing factors. In order to improve the forecast accuracy of gold price, this paper puts forward a gold price forecast model combing projection pursuit with neural network. At first, projection pursuit algorithm is used to screen the influencing factors, and then the influencing factors are used as the input ...

In this paper we introduce a new method for robust principal component analysis. Classical PCA is based on the empirical covariance matrix of the data and hence it is highly sensitive to outlying observations. In the past, two robust approaches have been developed. The first is based on the eigenvectors of a robust scatter matrix such as the MCD or an S-estimator, and is limited to relatively l...