Ohio State University

About: Ohio State University is a education organization based out in Columbus, Ohio, United States. It is known for research contribution in the topics: Population & Cancer. The organization has 102421 authors who have published 222715 publications receiving 8373403 citations. The organization is also known as: Ohio State & The Ohio State University.

Measuring the Affective and Cognitive Properties of Attitudes: Conceptual and Methodological Issues

Abstract: Despite renewed interest in the affective and cognitive properties of attitudes, assessment of these constructs is plagued by a number of problems. Some techniques for overcoming these problems are outlined, and scales for assessing the affective and cognitive properties of attitudes are reported. Two studies examine the reliability and validity of these scales. Study 1 assesses the internal consistency and the discriminant and convergent validity of these scales and indicates that the scales are useful for assessing the affective and cognitive properties of attitudes toward a wide range of objects. In Study 2, the ability of the scales to differentiate attitudes that are based primarily on affective versus cognitive information is examined by experimentally creating affective or cognitive attitudes in subjects. Analyses reveal that the scales can differentiate between people whose attitudes are based primarily on either affective or cognitive information.

Multiscale PCA with application to multivariate statistical process monitoring

Abstract: Multiscale principal-component analysis (MSPCA) combines the ability of PCA to decorrelate the variables by extracting a linear relationship with that of wavelet analysis to extract deterministic features and approximately decorrelate autocorrelated measurements. MSPCA computes the PCA of wavelet coefficients at each scale and then combines the results at relevant scales. Due to its multiscale nature, MSPCA is appropriate for the modeling of data containing contributions from events whose behavior changes over time and frequency. Process monitoring by MSPCA involves combining only those scales where significant events are detected, and is equivalent to adaptively filtering the scores and residuals, and adjusting the detection limits for easiest detection of deterministic changes in the measurements. Approximate decorrelation of wavelet coefficients also makes MSPCA effective for monitoring autocorrelated measurements without matrix augmentation or time-series modeling. In addition to improving the ability to detect deterministic changes, monitoring by MSPCA also simultaneously extracts those features that represent abnormal operation. The superior performance of MSPCA for process monitoring is illustrated by several examples.

The Fifth Data Release of the Sloan Digital Sky Survey

Abstract: This paper describes the Fifth Data Release (DR5) of the Sloan Digital Sky Survey (SDSS). DR5 includes all survey quality data taken through 2005 June and represents the completion of the SDSS-I project (whose successor, SDSS-II, will continue through mid-2008). It includes five-band photometric data for 217 million objects selected over 8000 deg^2 and 1,048,960 spectra of galaxies, quasars, and stars selected from 5713 deg^2 of that imaging data. These numbers represent a roughly 20% increment over those of the Fourth Data Release; all the data from previous data releases are included in the present release. In addition to "standard" SDSS observations, DR5 includes repeat scans of the southern equatorial stripe, imaging scans across M31 and the core of the Perseus Cluster of galaxies, and the first spectroscopic data from SEGUE, a survey to explore the kinematics and chemical evolution of the Galaxy. The catalog database incorporates several new features, including photometric redshifts of galaxies, tables of matched objects in overlap regions of the imaging survey, and tools that allow precise computations of survey geometry for statistical investigations.

Metric Learning: A Survey

Abstract: The metric learning problem is concerned with learning a distance function tuned to a particular task, and has been shown to be useful when used in conjunction with nearest-neighbor methods and other techniques that rely on distances or similarities. Metric Learning: A Review presents an overview of existing research in this topic, including recent progress on scaling to high-dimensional feature spaces and to data sets with an extremely large number of data points. It presents as unified a framework as possible under which existing research on metric learning can be cast. The monograph starts out by focusing on linear metric learning approaches, and mainly concentrates on the class of Mahalanobis distance learning methods. It then discusses nonlinear metric learning approaches, focusing on the connections between the non-linear and linear approaches. Finally, it discusses extensions of metric learning, as well as applications to a variety of problems in computer vision, text analysis, program analysis, and multimedia. Metric Learning: A Review is an ideal reference for anyone interested in the metric learning problem. It synthesizes much of the recent work in the area and it is hoped that it will inspire new algorithms and applications.