Principal Component Analysis (PCA) is one of the most valuable results oriented techniques of applied linear algebra. The minimum effort of PCA provides a roadmap for reducing a complex data set to a lower dimension to reveal the sometimes hidden, simplified structure that often underlie it. Bioelectrical signals express the electrical functionality of different organs in the human body. The El...

Speech denoising is very important in many applications where noise is unavoidable. The speech accuracy reduces strictly when the systems are operated in noisy environments. There are different Speech enhancement methods, a generalized form of Principal Component Analysis (PCA) is used for speech enhancement. A PCA based algorithm is proposed for denoising of speech degraded by noise interferen...

The problem of ranking features computed by principal component analysis (PCA) in N-class problems have been addressed by the multi-class discriminant principal component analysis (MDPCA) and the Fisher discriminability criterion (FDC). These methods are motivated by the fact that PCA components do not necessarily represent important discriminant directions to separate sample groups. Given a da...

Principal coordinate analysis (PCO), as a duality of principal component analysis (PCA), is also a classical method for exploratory data analysis. In this paper we propose a probabilistic PCO by using a normal latent variable model in which maximum likelihood estimation and an expectation-maximization algorithm are respectively devised to calculate the configurations of objects in a lowdimensio...

This paper describes the integration of the Principal Component Analysis into the Visualization Process. Although, the combination of Principal Component Analysis (PCA) and visual methods is a common approach to the analysis of high-dimensional datasets, it is mostly limited to a pure preprocessing step for dimension reduction. In this paper we will discuss, how PCA results can be used to contr...

Neural gas network is a single-layered soft competitive neural network, which can be applied to clustering analysis with fast convergent speed comparing to Self-organizing Map (SOM), K-means etc. Combining neural gas with principal component analysis, this paper proposes a new clustering method, namely principal components analysis neural gas (PCA-NG), and the online learning algorithm is also ...

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...