Principal Component Analysis ( Pca )

Principal Components Analysis (PCA) versus Principal Axes Factors (PAF) and Other Extraction Methods Broadly, conducting factor analysis (FA) allows a researcher to analyze or interpret his or her data (e.g., measured variables) by reducing those variables into factors or components that underlie the structure or explain the greatest amount of variance in the data (Thompson, 2004). Thompson (2004) also tells us that FA may be used for many purposes, the most common of which is to uncover a relationship

basic frame-work of the principal component analysis and fuzzy logic, along with some of the key basic concepts. A. The principal component analysis (PCA) The Principal component analysis (PCA) is an essential technique in data compression and feature reduction [13] and it is a statistical technique applied to reduce a set of correlated variables to smaller uncorrelated variables to each other. PCA is considered as special transformation which produces the principal components (PCs) Known as eigenvectors

Buffalo Creek basins over a 15-year period was obtained from The City of Greensboro Stormwater Division, North Carolina. The sampled data were grouped in ranges of years from 1999-2002, 2003-2008, 2009-2010 and 2011-2013 so as to obtain a detailed analysis on the data. The sampling sites in the study area were numbered for simplicity of result presentation. Sites 1 to 6 were located at the highly sub-urban and agricultural area and sites 7 to 18 were located in the highly urbanized area of Greensboro

in data analysis involved carrying out frequency distributions and cross-tabulations to understand how the sample was distributed across the selected predictors of educational attainment, which was measured by the four educational transitions. Inclusion of Chi-square test further helped to assess for existence of association between the independent and dependent variables. 3.5.2 Construction of wealth index and data reduction for household no-income variables: principal component analysis Factor

PCA model Principle component analysis (PCA) is often used to reduce the dimensionality of a data set, and the reduced data can then explain most of the variance within the original data (Guo, Wang & Louie, 2004). The main function of the PCA is to convert a number of interrelated variables into a smaller set of independent variables. The new independent variables which are called principal components (PCs). They are the linear combinations of the original variables (Jackson & J.E., 2005). The PCA

This paper presents a image fusion technique based on PCA and fuzzy logic. the framework of the proposed image fusion technique is divided in the following major phases:  preprocesing phase  Feature extraction based on the principal component analysis  The image fusion based on fuzzy set  Reconstruction final image The figure (1) shows the framework of the proposed image fusion and its phases. Fig. 1. The proposed approach of image fusion phases A. Preprocessing Phase This phase consists of three

Sudeep Sarkar et.al.[10] Researchers have suggested that the ear may have advantages over the face for biometric recognition. Our previous experiments with ear and face recognition, using the standard principal component analysis approach, showed lower recognition performance using ear images. We report results of similar experiments on larger data sets that are more rigorously controlled for relative quality of face and ear images. We find that recognition performance is not significantly different

Research in the field of watermarking is flourishing providing techniques to protect copyright of intellectual property. Among the various methods that exploits the characteristics of the Human Visual System (HVS) for more secure and effective data hiding, wavelet based watermarking techniques shows to be immune to attacks, adding the quality of robustness to protect the hidden message of third party modifications. In this paper, we introduced non blind with DWT & SVD . Also we applies a casting

concept being studied (Aday & Cornelius, 2006). In order to assist this first step, definitions of the three constructs; collaboration, communication and trust will be given to the experts. A Content Validity Index will be used to assist in this analysis (Table 1). Evaluating a scale’s content validity is a critical early step in enhancing the overall validity of an instrument (Beck & Polit, 2006; Beck, Owen & Polit, 2007). As mentioned above, content validity concerns the degree to which a scale

There has been an explosive growth in use of internet and World Wide Web and also in multimedia technology and it’s applications recently. This has facilitated the distribution of the digital contents over the internet. Digital multimedia works (video, audio and images) become available for retransmission, reproduction, and publishing over the Internet. A large amount of digital data is duplicated and distributed without the owner’s consent. This arises a real need for protection against unauthorized