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Standard high-dimensional regression methods assume that the underlying coefficient vector is sparse. This might not be true in some cases, in particular in presence of hidden, confounding variables. Such hidden confounding can be represented as a high-...

This work introduces a computational framework for applying absolute electrical impedance tomography to head imaging without accurate information on the head shape or the electrode positions. A library of 50 heads is employed to build a principal ...

We show that prediction performance for global-local shrinkage regression can overcome two major difficulties of global shrinkage regression: (i) the amount of relative shrinkage is monotone in the singular values of the design matrix and (ii) the ...

The generalized T2 chart GT-chart, which is composed of the T2 statistic based on a small number of principal components and the remaining components, is a popular alternative to the traditional Hotelling's T2 control chart. However, the application of ...

This paper focuses on techniques for dealing with imperfect data in a frame of early warning system (EWS). Despite the fact that data may be technically damaged by presenting noise, outliers or missing values, met-ocean simulation systems have to deal ...

In this paper, we propose a continuous-domain version of principal-component analysis, with the constraint that the underlying family of templates appears at arbitrary orientations. We show that the corresponding principal components are steerable. Our ...

The Ras enzyme mediates critical signaling pathways in cell proliferation and development by transitioning between GTP- (active) and GDP-bound (inactive) states. Many cancers are linked to specific Ras mutations affecting its conformational switching ...

This paper presents a modified geometric hashing technique to index the database of facial images. The technique makes use of minimum amount of search space and memory to provide best matches with high accuracy against a query image. Features are ...

Many important kernel methods in the machine learning area, such as kernel principal component analysis, feature approximation, denoising, compression, and prediction require the computation of the dominant set of eigenvectors of the symmetric kernel ...

Association rule mining is an important data mining task that discovers relationships among items in a transaction database. Most approaches to association rule mining assume that all items within a dataset have a uniform distribution with respect to ...

Random Spherical Linear Oracles (RSLO) for DNA microarray gene expression data are proposed for classifier fusion. RSLO employs random hyperplane splits of samples in the principal component score space based on the first three principal components (X, ...

We present eigenvalue bounds for perturbations of Hermitian matrices and express the change in eigenvalues in terms of a projection of the perturbation onto a particular eigenspace, rather than in terms of the full perturbation. The perturbations we ...

A reduced order representation of a large data set is often realized through a principal component analysis based upon a singular value decomposition (SVD) of the data. The left singular vectors of a truncated SVD provide the reduced basis. In several ...

We present a new approach to univariate partial least squares regression (PLSR) based on directional signal-to-noise ratios (SNRs). We show how PLSR, unlike principal components regression, takes into account the actual value and not only the variance ...

Principal components are a well established tool in dimension reduction. The extension to principal curves allows for general smooth curves which pass through the middle of a multidimensional data cloud. In this paper local principal curves are ...

FastMap is a dimension reduction technique that operates on distances between objects. Although only distances are used, implicitly the technique assumes that the objects are points in a p\hbox{-}{\rm{dimensional}} Euclidean space. It selects a sequence ...

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. In this article, we develop multivariate techniques for visualizing gene regulatory networks using independent components ...

Starting from a model of the within-die systematic variations using principal components analysis, a model is proposed for estimation of the parametric yield, and is then applied to estimation of the timing yield. Key features of these models are that ...

This paper deals with correlation tests from the class of spherical tests introduced by Läuter (Biometrics 52 (1996) 964). These methods provide an alternative to classical MANOVA approaches and are particularly useful in small samples. Following a ...

In this paper we propose two new descriptive measures for multivariate data: the effective variance and the effective dependence. These measures have a direct geometric and statistical interpretation and can be used to compare groups with different ...