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About 10,000 papers for “data reduction”

DATA REDUCTION

2019

This chapter identifies the differences in dimensionality reduction based on features, cases, and reduction of value techniques. It explains the advantages of data reduction in the preprocessing phase of a data-mining process. The chapter compares entropy-based technique and principal component analysis for feature ranking. For really large data sets, there is an increased likelihood that an intermediate, additional step—data reduction—should be performed prior to applying the data-mining techniques. Feature selection algorithms typically fall into two categories: feature ranking and subset selection. Examples of additional methodologies in feature extraction include factor analysis, independent component analysis, and multidimensional scaling. The core of the Relief algorithm is to estimate the quality of features according to how well their values distinguish between samples close to each other. A method for unsupervised feature selection or ranking based on entropy measure is a relatively simple technique; but with a large number of features its complexity increases significantly.

Cited by 0

Data Reduction

Rui Zhang · Encyclopedia of Database Systems · 2016

Cited by 1

Data Reduction

Society of Exploration Geophysicists eBooks · 2012

Abstract Lateral variations in the density of rocks cause variations in the gravity field measured at the surface, and our central problem in gravity exploration is to discover the nature of subsurface rocks, their constituents, their structure, and their distribution. Toward this end, we use the theory and tools developed and described in the first five chapters. In general, the observed gravity value g ois equal to the sum of the gravity anomaly g acaused by the geologic masses we wish to study and the contribution resulting from “noise,” g N. For present purposes, we will define the noise contribution, as the sum of all unwantedeffects, where g prepresents all the effects caused by variations in position, elevation, speed of the instrument, and so forth, for which standard corrections apply; g grepresents geologic noise effects caused by unknown or uncertain geologic features other than our target(s) of interest (discussed in Chapters 7 and 8); g irepresents untreated instrumental noise, such as nonlinear drift components in the instrument; and g dincludes survey design noise (aliasing), as shown in Figure 10of Chapter 4. In the data-reduction phase of gravity work, our goal is to identify and remove the effects that make up the first term on the right side of equation 1, g p, and to evaluate the potential magnitudes of the last two terms, g tand g d. The intended result is an anomaly field in which all the unwanted contributions to measured gravity have been partly

Cited by 0

Data Reduction

Thavisha Dharmawardena · Figshare · 2019

Instructions to download the SCUBA-2 and PACS data. Instructions to reduce the SCUBA-2 data and the reduced data products.

Cited by 0Open access

Data Reduction

Rui Zhang · Encyclopedia of Database Systems · 2018

Cited by 0

Data Reduction

2020

Before data analysis can commence, the raw experimental data collected in each individual neutron detector needs to be ‘reduced’; a process that involves applying a standard set of mathematical procedures to correct for, or remove, unwanted instrument, scattering and/or source contributions. This chapter therefore considers the basic data reduction procedure; from normalisation, masking and detector efficiency correction to absorption and multiple scattering considerations. Differences regarding the reduction of data collected using different QENS instrument classes are highlighted.

Cited by 0

Data Reduction

Thavisha Dharmawardena · Figshare · 2019

Instructions to download the SCUBA-2 and PACS data. Instructions to reduce the SCUBA-2 data and the reduced data products.

Cited by 0Open access

Visar data reduction

L. M. Barker · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1974

The data reduction theory that allows both specimen surface velocity and surface velocity distribution to be determined from the data of the Velocity Interferometer System for Any Reflector (VISAR) is presented. Two time-sharing, user-interactive computer programs that implement the theory are described, and the program listings are provided. (auth)

Cited by 3Open access

Infrasonic Data Reduction

George Ohring · 1970

Abstract : A multi-channel prediction-error filter technique is developed for suppressing noise on infrasonic signals. The technique uses samples of noise prior to a signal for deriving a Wierner prediction filter that is used to predict the noise during the first motion of the infrasonic signal. A computer program entitled MAXLKH is written to carry out the filtering technique. Application of the technique to actual infrasonic records indicates that noise has some degree of predictability and, hence, an enhancement of the infrasonic signal results. Further tests are suggested to quantify the amount of noise suppression and to optimize technique parameters such as filter length and prediction span. A discussion of the computer program is included.

Cited by 0