frost is one of the atmospheric phenomena which seriously threaten crop production. it also causes numerousaccidents in mountainous roads. in this research the spatial synoptic classification ssc method was employed toclassify the type of air masses. for the classification, such meteorological data as: temperature, dew point, mean sealevel pressure, cloudiness, direction and speed of wind were ...

the analysis of geochemical data in frequency domain, as indicated in this research study, can provide new exploratory informationthat may not be exposed in spatial domain. to identify deep geochemical anomalies, sulfide zone and geochemical noises in dalli cu–au porphyry deposit, a new approach based on coupling fourier transform (ft) and principal component analysis (pca) has beenused. the re...

This paper demonstrates a methodology of image enhancement that uses principle component analysis (PCA) in wavelet domain. PCA fully de-correlates the original data set so that the energy of the signal will concentrate on the small subset of PCA transformed dataset. The energy of random noise evenly spreads over the whole data set, we can easily distinguish signal from random noise over PCA dom...

By analyzing the direction characteristic of principal component analysis (PCA), we propose an edge detection method based on PCA. Using Karhunen–Loëve transform, PCA transforms the original dataset into lower-dimensional feature data. The transform has directivity both on energy accumulation and data selection. The author points out and proves the two direction characteristics. In this paper, ...

Linear and logistic regression are well-known data mining techniques, however, their ability to deal with interdependent variables is limited. Principal component analysis (PCA) is a prevalent data reduction tool that both transforms the data orthogonally and reduces its dimensionality. In this paper we explore an adaptive hybrid approach where PCA can be used in conjunction with logistic regre...

-We derive and discuss various generalizations of neural PCA (Principal Component Analysis)-type learning algorithms containing nonlinearities using optimization-based approach. Standard PCA arises as an optimal solution to several different information representation problems. We justify that this is essentially due to the fact that the solution is based on the second-order statistics only. I ...

A series of microarray experiments produces observations of differential expression for thousands of genes across multiple conditions. Principal component analysis(PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. PCA, which can be implemented...

This paper aims to provide a new method of visualizing high-dimensional data classification by employing principal component analysis (PCA) and support vector machine (SVM). In this method, PCA is adopted to reduce the dimension of high-dimensional data, and then SVM is used for the data classification process. At last, the classified result is projected to two-dimension mapping. The method can...