Distinct Variation Pattern Discovery Using Alternating Nonlinear Principal Component Analysis

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 ...

Improving gene expression cancer molecular pattern discovery using nonnegative principal component analysis.

Robust cancer molecular pattern identification from microarray data not only plays an essential role in modern clinic oncology, but also presents a challenge for statistical learning. Although principal component analysis (PCA) is a widely used feature selection algorithm in microarray analysis, its holistic mechanism prevents it from capturing the latent local data structure in the following c...

Semantic discovery of web services using principal component analysis

With the increase in the number of available web services, searching for appropriate web services fulfilling the service discoverer’s functional requirements has become as major challenge. Web services standards, in their present format support only keyword based search and many services which can fulfill the user’s requirements are not retrieved. Basic requirement for efficient service discove...

A Study of Nonlinear Principal Component Analysis Using Neural Networks