This paper presents a probabilistic analysis of kernel principal components by unifying the theory of probabilistic principal component analysis and kernel principal component analysis. It is shown that, while the kernel component enhances the nonlinear modeling power, the probabilistic structure offers (i) a mixture model for nonlinear data structure containing nonlinear sub-structures, and (i...

Many applications in computer vision use Principal Components Analysis (PCA), for example, in camera calibration, stereo, localization and motion estimation. We present a new and fast PCA-based method to analyze optical snow. Optical snow is a complex form of visual motion that occurs when an observer moves through a highly cluttered 3D scene. For this category of motion field, no spatial or de...

The authors provide a didactic treatment of nonlinear (categorical) principal components analysis (PCA). This method is the nonlinear equivalent of standard PCA and reduces the observed variables to a number of uncorrelated principal components. The most important advantages of nonlinear over linear PCA are that it incorporates nominal and ordinal variables and that it can handle and discover n...

The neural network, using an unsupervised generalized Hebbian algorithm (GHA), is adopted to find the principal eigenvectors of a covariance matrix in different kinds of seismograms. We have shown that the extensive computer results of the principal components analysis (PCA) using the neural net of GHA can extract the information of seismic reflection layers and uniform neighboring traces. The ...

in order to sustainable use of rangelands, it is necessary to recognize water, soil, and vegetation cover and analyze their relationships. physical and chemical properties of soil are effective in distribution of plant species in local area. to determine the effects of some soil properties on species distribution of eurotia ceratoides and stipa barbata in baghedar summer region of bafgh, three ...

Principal Component Analysis is a method for reducing the dimensionality of datasets while also limiting information loss. It accomplishes this by producing uncorrelated variables that maximize variance one after other. The accepted criterion evaluating Component’s (PC) performance λ_j/tr(S) where tr(S) denotes trace covariance matrix S. standard procedure to determine how many PCs should be ma...

this research was conducted to investigate genotyp×environment interaction for grain yield of ten wheat cultivars in 8 environments (combinations of two nitrogen fertilizer treatment and four irrigation regimes). analysis of additive main effects (analysis of variance) and multiplicative interaction effects (principal component analysis) revealed that the effects of genotype, environment and ge...