A clustering is process to identify a homogeneous groups of object called as cluster. Clustering is one interesting topic on data mining. A group or class behaves similarly characteristics. This paper discusses a robust clustering process for data images with two reduction dimension approaches; i.e. the two dimensional principal component analysis (2DPCA) and principal component analysis (PCA)....

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...

Principal component analysis (PCA) is a dimensionality reduction and data analysis tool commonly used in many areas. The main idea of PCA is to represent high-dimensional data with a few representative components that capture most of the variance present in the data. However, there is an obvious disadvantage of traditional PCA when it is applied to analyze data where interpretability is importa...

Table 1. June water chemistry values for Nine Pipe and Ovando Valley wetland study sites. Data from Borth (1998). Table 2. July water chemistry values for Nine Pipe and Ovando Valley wetland study sites. Data from Borth (1998). Table 3. August water chemistry values for Nine Pipe and Ovando Valley wetland study sites. Data from Borth (1998). Table 4. June, July and August average water chemistr...

In this paper we present a comparison of three projection methods that use the eigenvectors of a matrix to investigate high-dimensional dataset: principal component analysis (PCA), principal component analysis followed by independent component analysis (PCA+ICA), and Laplacian projections. We demonstrate the application of these methods to a sponsored links search listings dataset and provide a...

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...

Abstract. a novel method of fault diagnosis for power electronics rectifier based on PCA-SVM is presented in this paper. First, the features of the fault was extracted by principal component analysis (PCA), an SVM algorithm was introduced to train the SVM for diagnosis. Experimental results presented in this paper show that the diagnostic method has very good diagnostic ability and efficient; a...

This article studies and describes a monitoring, fault detection, and diagnosis technique based on the unfolded PCA (UPCA) approach and its application to a reverse osmosis desalination plant. The UPCA approach is normally applied to batch processes, but in this case, the UPCA approach is applied to a continuous process, which does not present a strict steady state. The classical principal comp...

this paper presents an integrated data envelopment analysis (dea) – principal component analysis (pca) – analytical hierarchy process (ahp) to achieve the efficiency scores and ranks of the insurance companies. fourteen insurance companies with thirteen input and output variables have been considered for the purpose of this study. since the dea model is sensitive to the number of variables in c...

Recently, fault detection and process monitoring using principal component analysis (PCA) were studied intensively and largely applied to industrial process. PCA is the optimal linear transformation with respect to minimizing the mean squared prediction error. If the data have nonlinear dependencies, an important issue is to develop a technique which can take into account this kind of dependenc...