Principal component analysis (PCA) is usually used for compressing information in multivariate data sets by computing orthogonal projections that maximize the amount of data variance. PCA is effective if the multivariate data set is a vector with Gaussian distribution. But multi-spectral images data sets are not probably submitted to such Gaussian distribution. The paper proposes a method based...

Principal component analysis (PCA) is a popular technique to reduce the dimension of the data at hand. Since PCA is based on the empirical variance-covariance matrix, the estimates can be severely damaged by outliers. To reduce these effects, several robust methods were developed, mostly by replacing the classical variance-covariance matrix by a robust version. In this paper we focus on Stahel-...

SUNGKYU JUNG: Asymptotics for High Dimension, Low Sample Size data and Analysis of Data on Manifolds. (Under the direction of Dr. J. S. Marron.) The dissertation consists of two research topics regarding modern non-standard data analytic situations. In particular, data under the High Dimension, Low Sample Size (HDLSS) situation and data lying on manifolds are analyzed. These situations are rela...

Tree-dependent component analysis (TCA) is a generalization of independent component analysis (ICA), the goal of which is to model the multivariate data by a linear transformation of latent variables, while latent variables fit by a tree-structured graphical model. In contrast to ICA, TCA allows dependent structure of latent variables and also consider non-spanning trees (forests). In this pape...

In this paper, we propose an unsupervised approach for identifying bipolar person names in a set of topic documents. We employ principal component analysis (PCA) to discover bipolar word usage patterns of person names in the documents and show that the signs of the entries in the principal eigenvector of PCA partition the person names into bipolar groups spontaneously. Empirical evaluations dem...

We derive a learning algorithm for inferring an overcomplete basis by viewing it as probabilistic model of the observed data. Over-complete bases allow for better approximation of the underlying statistical density. Using a Laplacian prior on the basis coeecients removes redundancy and leads to representations that are sparse and are a nonlinear function of the data. This can be viewed as a gen...

the study area covers the northern part of the baft geological map (scale of 1:100 000 ). several porphyry and vein-type mineralization are reported from this area. a topic that is discussed in the mineral exploration community is the use of remote sensing and airborne geophysics for porphyry type mineralization. which one is more reliable and efficient in hydrothermal alteration mapping? airbo...

Annular patterns with a high degree of zonal symmetry play a prominent role in the natural variability of the atmospheric circulation and its response to external forcing. But despite their apparent importance for understanding climate variability, the processes that give rise to their marked zonally symmetric components remain largely unclear. Here the authors use simple stochastic models in c...