Results 41 to 50 of about 364,885 (315)

Dynamics of dimensional reduction

Physics Letters B, 1980
In d-dimensional unified theories that, along with gravity, contain an antisymmetric tensor field of rank s-1, preferential compactification of d-s or of s space-like dimensions is found to occur. This is the case in 11-dimensional supergravity where s = 4.
Mark A. Rubin, Peter G. O. Freund
openaire   +2 more sources

Non-Redundant Spectral Dimensionality Reduction

, 2017
Spectral dimensionality reduction algorithms are widely used in numerous domains, including for recognition, segmentation, tracking and visualization.
A Brun, A Hyvärinen, A Hyvärinen, A Singer, B Schölkopf, C Jutten, CC Chang, DL Donoho, EA Nadaraya, G Guo, GS Watson, JB Tenenbaum, L Maaten Van Der, M Belkin, M Belkin, M Rubinstein, MS Bartlett, N Halko, P Isola, RR Coifman, ST Roweis, X Geng, X He, Y Goldberg, Y LeCun, Z Zhang, Z Zhang   +26 more
core   +1 more source

Dimensionality reduction method for hyperspectral image analysis based on rough set theory

European Journal of Remote Sensing, 2020
High-dimensional features often cause computational complexity and dimensionality curse. Feature selection and feature extraction are the two mainstream methods for dimensionality reduction.
Zhenhua Wang, Suling Liang, Lizhi Xu, Wei Song, Dexing Wang, Dongmei Huang   +5 more
doaj   +1 more source

Arabic L2 readability assessment: Dimensionality reduction study

Journal of King Saud University: Computer and Information Sciences, 2022
Readability is a measure that associates a written text to a reader’s skill or grade level. Readability assessment is very important in the field of second or foreign language (L2) learning.
Naoual Nassiri, Abdelhak Lakhouaja, Violetta Cavalli-Sforza   +2 more
doaj   +1 more source

Branched polymers and dimensional reduction [PDF]

Annals of Mathematics, 2003
We establish an exact relation between self-avoiding branched polymers in D+2 continuum dimensions and the hard-core continuum gas at negative activity in D dimensions. We review conjectures and results on critical exponents for D+2 = 2,3,4 and show that they are corollaries of our result. We explain the connection (first proposed by Parisi and Sourlas)
David C. Brydges, John Z. Imbrie
openaire   +4 more sources

Non-negative Dimensionality Reduction for Mammogram Classification [PDF]

Journal of Electrical and Electronics Engineering, 2009
Directly classifying high dimensional datamay exhibit the ``curse of dimensionality'' issue thatwould negatively influence the classificationperformance with an increase in the computationalload, depending also on the classifier structure.
I. Buciu, A. Gacsadi
doaj  

Dimensionality reduction in data from LASER applications [PDF]

مجلة جامعة الانبار للعلوم الصرفة, 2012
Redundant variables not only in LASER applications, but in all experimental works are disturbing statistical analysis as a result of highly correlation among them.
Imad H.Aboud, Qassim M. Jameel
doaj   +1 more source

AN ALTERNATIVE DIMENSIONAL REDUCTION PRESCRIPTION [PDF]

Modern Physics Letters A, 1996
We propose an alternative dimensional reduction prescription which in respect with Green functions corresponds to dropping the extra spatial coordinate. From this, we construct the dimensionally reduced Lagrangians both for scalars and fermions, discussing bosonization and supersymmetry in the particular two-dimensional case.
Edelstein Glaubach, José Daniel, Giambiagi, J.J., Núñez, Carlos, Schaposnik, Fidel Arturo   +3 more
openaire   +3 more sources

Quantum resonant dimensionality reduction

Physical Review Research
Quantum computing is a promising candidate for accelerating machine learning tasks. Limited by the control accuracy of current quantum hardware, reducing the consumption of quantum resources is the key to achieving quantum advantage.
Fan Yang, Furong Wang, Xusheng Xu, Pan Gao, Tao Xin, ShiJie Wei, Guilu Long   +6 more
doaj   +1 more source

Linear Dimensionality Reduction: What Is Better?

Data
This research paper focuses on dimensionality reduction, which is a major subproblem in any data processing operation. Dimensionality reduction based on principal components is the most used methodology. Our paper examines three heuristics, namely Kaiser’
Mohit Baliyan, Evgeny M. Mirkes
doaj   +1 more source