Principal component analysis summarizes high dimensional data into a few dimensions. Each dimension is called a principal component and represents a linear combination of the variables. The first ...

The generalized linear model (Nelder & Wedderburn, 1972) has become an elegant and practical option to classical least-squares linear model building. We consider the specific problem of generalized ...

Image processing field is becoming more popular for the security purpose in now-a-days. It has many sub fields and face recognition is one from them. Many techniques have been developed for the face ...

A set of desert vegetation-environment data consisting of 22 concrete communities in Southern Sind was analyzed with two multivariate methods, viz. canonical correlation analysis (CCA) and principal ...