Principal Component Analysis for the Nonlinear Portfolio Model

Nonlinear Principal Component Analysis

A. Two quite different forms of nonlinear principal component analysis have been proposed in the literature. The first one is associated with the names of Guttman, Burt, Hayashi, Benzécri, McDonald, De Leeuw, Hill, Nishisato. We call itmultiple correspondence analysis. The second form has been discussed by Kruskal, Shepard, Roskam, Takane, Young, De Leeuw, Winsberg, Ramsay. We call it no...

Nonlinear principal component analysis

We study the extraction of nonlinear data models in high dimensional spaces with modi ed self organizing maps We present a general algorithm which maps low dimensional lattices into high dimensional data manifolds without violation of topology The approach is based on a new principle exploiting the speci c dynamical properties of the rst order phase tran sition induced by the noise of the data ...

Principal component analysis for nonlinear model reference adaptive control

A nonlinear adaptive control strategy based on radial basis function networks and principal component analysis is presented. The proposed method is well suited for low dimensional nonlinear systems that are difficult to model and control via conventional means. The effective system dimension is reduced by applying nonlinear principal component analysis to state variable data obtained from open-...

Projection techniques for nonlinear principal component analysis

Sparse Structured Principal Component Analysis and Model Learning for Classification and Quality Detection of Rice Grains

In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification and quality detection in this paper is presented based on model learning concepts includ...