We propose shiny analysis framework accompanied with makeup deterioration using normalized facial images (MaVIC and the corresponding makeup-deteriorated data sets). These images are analyzed and reconstructed based on principal component analysis (PCA) and then the differential ones between the reconstruction images with different numbers of PCA components can be generated. The shiny Eigen-fac...

In this paper, in order to categorize ORL database face pictures, principle Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) methods by using Elman neural network and Support Vector Machine (SVM) categorization methods are used. Elman network as a recurrent neural network is proposed for modeling storage systems and also it is used for reviewing the effect of using PCA nu...

detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks (wsns). to address the problem of outlier detection in wireless sensor networks, in this paper we present a pca-based centralized approach and a dpca-based distributed energy-efficient approach for detecting outliers in sensed data in a wsn. the outliers in sensed data can be ca...

linseed is an important oilseed and fibre crop predominantly grown in india. the aim of the present research was to evaluate genetic diversity and patterns of relationships among the 58 genotypes through 10 morphological traits and 12 polymorphic microsatellite (ssr) markers. euclidean analysis of agro-morphological traits grouped the 58 genotypes into four clusters of which cluster i was the l...

With the increasing of non-linear, burst or un-balanced load, power quality issues in the grid is becoming important. With more power quality monitors installed with higher sampling rates, an expanded size of power quality data brings difficulty to storage, transmission and analysis. In this paper, principal component analysis (PCA), which is a popular feature extraction algorithm in pattern re...

In this paper, we use sparse principal component analysis (PCA) to solve clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combinations of the data variables, explaining a maximum amount of variance in the data while having only a limited number of nonzero coefficients. PCA is often used as a simple clustering technique and sparse factors allow us here to int...

Principal component analysis (PCA) is often used for analyzing data in the most diverse areas. In this work, we report an integrated approach to several theoretical and practical aspects of PCA. We start by providing, intuitive accessible manner, basic principles underlying PCA its applications. Next, present a systematic, though no exclusive, survey some representative works illustrating poten...

selecting within local pomegranate accessions is the main method used to identify new cultivars. total of 76 pomegranate accessions from hatay, turkey, were collected and their morpho-pomological and chemical characteristics were determined. the results showed that there was significant diversity among the accessions in terms of fruit quality parameters. several accessions were notable for thei...

Neural network has been popular in time series prediction in financial areas, because of their advantages in handling nonlinear systems. This paper hybridizes genetic algorithm and artificial neural network method (GABP), and hybridizes principal component analysis and support vector machine (PCA-SVM) to predict the next opening price in stock markets. Principal component analysis method is app...

induction motor bearing is one of the key parts of the machine and its analysis and interpretation are important for fault detection. in the present work vibration signal has been taken for the classification i.e. bearing is healthy (h) or defective (d). for this purpose, clustering based classification of bearing vibration data has been carried out using principal component analysis (pca) and ...