In this paper, an improved version of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) is proposed for feature extraction to classify the ischemic beats from electrocardiogram (ECG) signal. The Fuzzy C-Means (FCM) and Genetic Algorithm (GA) is combined with PCA and ICA to extract more relevant features; the proposed methods are named as Fuzzy-Genetic based PCA (FGPCA)...

In this paper Principal Components Analysis (PCA) is used for detecting faults in a simulated wastewater treatment plant (WWTP). PCA is a multivariate statistical technique used in multivariate statistical process control (MSPC) and fault detection and isolation (FDI) perspectives. PCA reduces the dimensionality of the original historical data by projecting it onto a lower dimensionality space....

688 Abstract—Principal Component Analysis (PCA) is a statistical technique used for dimension reduction and recognition, & widely used for facial feature extraction and recognition. In this paper a cluster based SPCA face recognition method has been proposed. Experiments based on ORL face database have performed to compare the recognition rate between tradition PCA, Advanced principal component...

The recurrent least squares (RLS) learning approach is proposed for controlling the learning rate in parallel principal subspace analysis (PSA) and in a wide class of principal component analysis (PCA) associated algorithms with a quasi{parallel extraction ability. The purpose is to provide a useful tool for applications where the learning process has to be repeated in an on{line self{adaptive ...

The recurrent least squares (RLS) learning approach is proposed for controlling the learning rate in parallel principal subspace analysis (PSA) and in a wide class of principal component analysis (PCA) associated algorithms with a quasi{parallel extraction ability. The purpose is to provide a useful tool for applications where the learning process has to be repeated in an on{line self{adaptive ...

In this paper we proposed an iterative elimination algorithm for sparse principal component analysis. It recursively eliminates variables according to certain criterion that aims to minimize the loss of explained variance, and reconsiders the sparse principal component analysis problem until the desired sparsity is achieved. Two criteria, the approximated minimal variance loss (AMVL) criterion ...

In analogy to the PCA setting, the sparse PCA problem is often solved by iteratively alternating between two subtasks: cardinality-constrained rank-one variance maximization and matrix deflation. While the former has received a great deal of attention in the literature, the latter is seldom analyzed and is typically borrowed without justification from the PCA context. In this work, we demonstra...

The widely used principal component analysis (PCA) is implemented in nonlinear by an auto-associative neural network. Compared to other nonlinear versions, such as kernel PCA, such a nonlinear PCA has explicit encoding and decoding processes, and the data can be transformed back to the original space. Its data compression performance is similar to that of PCA, but data analysis performance such...