In this paper, we assume that word co-occurrence statistics can be used to extract meaningful features, exhibiting syntactic and semantic behavior, from text data. Independent component analysis (ICA), an unsupervised statistical method, is applied to word usage statistics, calculated from a natural language corpora, to extract a number of features. With a self-organizing map (SOM), we will dem...

This chapter presents an introduction to robust statistics with applications of a chemometric nature. Following a description of the basic ideas and concepts behind robust statistics, including how robust estimators can be conceived, the chapter builds up to the construction (and use) of robust alternatives for some methods for multivariate analysis frequently used in chemometrics, such as prin...

spatial patterns are useful descriptors of the horizontal structure in a plant population and may change over time as the individual components of the population grow or die out. but, whether this is the case for desert woody annuals is largely unknown. in the present investigation, the variations in spatial patterns of tribulus terrestris during different pulse events in semi-arid area of the ...

the writers of this research have studied seven variables including: precipitation, relative humidity, sunny hours, temperature average, temperature minimum, temperature maximum and sea level pressure, at sanandaj air station during 1964-1994. sanandaj air station has nearly 10966 days of complete data for these variables. a principal component analysis (pca) has been applied on this data; then...

background: metabolic syndrome (mets), a combination of coronary heart disease and diabetes mellitus risk factor, refer to one of the most challenging public health issues in worldwide. the aim of this study was to identify the subgroups of participants in a study on the basis of mets components.   methods: the cross-sectional study took place in the districts related to tehran university of me...

abstract soil quality evaluation is an essential issue in soil management for agriculture and natural resource protection. soil quality indices are useful tools for determination and comparison of soils quality. using of principle component analysis in this study we selected 6 important properties as a soil quality minimum data set (mds) among 18 soil properties (tds). then, soil quality of agr...

introduction: the accuracy of analyzing functional mri (fmri) data is usually decreases in the presence of noise and artifact sources. a common solution in for analyzing fmri data having high noise is to use suitable preprocessing methods with the aim of data denoising. some effects of preprocessing methods on the parametric methods such as general linear model (glm) have previously been evalua...

the purpose of this study was to describe the association between grain yield and its components. the experiment was conducted in a randomized complete block design with three replicatsions. results of analysis showed that similar trends were observed for genotypic and phenotypic correlations and in most cases, the value for genetic correlation was greater than phenotypic value. 100-seed weight...

when the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. in this paper, we propose a supervised feature extraction method based on discriminant analysis (da) which uses the first principal component (pc1) to weight the scatter matrices. the proposed method, called da-pc1, copes with the small sample size problem and has...

t is the purpose of this paper to contribute to the discussion initiated bywachter about the parallelism between principal component (pc) and atypological grade of membership (gom) analysis. the author testedempirically the close relationship between both analysis in a lowdimensional framework comprising up to nine dichotomous variables and twotypologies. our contribution to the subject is also...