Study of Influence of Dimension Reduction of High Dimensional Datasets in Classification Problem

2020 ◽  
Author(s):  
Mohd. Salman Hossain Bhuiyan ◽  
Nabil Al Raian ◽  
Shahad Iqbal Leon ◽  
Musharrat Khan
Keyword(s):  
Dimension Reduction ◽  
Classification Problem ◽  
High Dimensional ◽  
High Dimensional Datasets

scholarly journals Generalized penalty for circular coordinate representation

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Hengrui Luo ◽  
Alice Patania ◽  
Jisu Kim ◽  
Mikael Vejdemo-Johansson
Keyword(s):  
Dimension Reduction ◽  
Real Data ◽  
Topological Data Analysis ◽  
High Dimensional ◽  
Topological Structures ◽  
Sampling Schemes ◽  
Coordinate Representation ◽  
Geometrical Shapes ◽  
Persistent Cohomology ◽  
High Dimensional Datasets

<p style='text-indent:20px;'>Topological Data Analysis (TDA) provides novel approaches that allow us to analyze the geometrical shapes and topological structures of a dataset. As one important application, TDA can be used for data visualization and dimension reduction. We follow the framework of circular coordinate representation, which allows us to perform dimension reduction and visualization for high-dimensional datasets on a torus using persistent cohomology. In this paper, we propose a method to adapt the circular coordinate framework to take into account the roughness of circular coordinates in change-point and high-dimensional applications. To do that, we use a generalized penalty function instead of an <inline-formula><tex-math id="M1">\begin{document}$ L_{2} $\end{document}</tex-math></inline-formula> penalty in the traditional circular coordinate algorithm. We provide simulation experiments and real data analyses to support our claim that circular coordinates with generalized penalty will detect the change in high-dimensional datasets under different sampling schemes while preserving the topological structures.</p>

An Ant Colony Optimization Based Dimension Reduction Method for High-Dimensional Datasets

2013 ◽  
Vol 10 (2) ◽  
pp. 231-241 ◽  
Author(s):  
Ying Li ◽  
Gang Wang ◽  
Huiling Chen ◽  
Lian Shi ◽  
Lei Qin
Keyword(s):  
Dimension Reduction ◽  
Ant Colony Optimization ◽  
Reduction Method ◽  
Ant Colony ◽  
High Dimensional ◽  
Dimension Reduction Method ◽  
High Dimensional Datasets

scholarly journals Dimensionality Reduction Algorithms on High Dimensional Datasets

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Iwan Syarif
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Particle Swarm ◽  
Classification Problem ◽  
Classification Performance ◽  
High Dimensional ◽  
Selection Algorithms ◽  
High Dimensional Datasets ◽  
Better Than

Classification problem especially for high dimensional datasets have attracted many researchers in order to find efficient approaches to address them. However, the classification problem has become very complicatedespecially when the number of possible different combinations of variables is so high. In this research, we evaluate the performance of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as feature selection algorithms when applied to high dimensional datasets.Our experiments show that in terms of dimensionality reduction, PSO is much better than GA. PSO has successfully reduced the number of attributes of 8 datasets to 13.47% on average while GA is only 31.36% on average. In terms of classification performance, GA is slightly better than PSO. GA‐ reduced datasets have better performance than their original ones on 5 of 8 datasets while PSO is only 3 of 8 datasets.Keywords: feature selection, dimensionality reduction, Genetic Algorithm (GA), Particle Swarm Optmization (PSO).

A new method for mining disjunctive emerging patterns in high-dimensional datasets using hypergraphs

2014 ◽  
Vol 40 ◽  
pp. 1-10 ◽  
Author(s):  
Renato Vimieiro ◽  
Pablo Moscato
Keyword(s):  
New Method ◽  
High Dimensional ◽  
Emerging Patterns ◽  
High Dimensional Datasets

Fast Algorithms for LS and LAD-Collaborative Regression

2021 ◽  
Author(s):  
Jun Sun ◽  
Lingchen Kong ◽  
Mei Li
Keyword(s):  
Numerical Experiments ◽  
Modern Science ◽  
Alternating Direction Method ◽  
Least Square ◽  
High Dimensional ◽  
Statistical Interpretation ◽  
Absolute Deviation ◽  
Linear Rate ◽  
Alternating Direction ◽  
High Dimensional Datasets

With the development of modern science and technology, it is easy to obtain a large number of high-dimensional datasets, which are related but different. Classical unimodel analysis is less likely to capture potential links between the different datasets. Recently, a collaborative regression model based on least square (LS) method for this problem has been proposed. In this paper, we propose a robust collaborative regression based on the least absolute deviation (LAD). We give the statistical interpretation of the LS-collaborative regression and LAD-collaborative regression. Then we design an efficient symmetric Gauss–Seidel-based alternating direction method of multipliers algorithm to solve the two models, which has the global convergence and the Q-linear rate of convergence. Finally we report numerical experiments to illustrate the efficiency of the proposed methods.

Dimension Reduction and Regularization Combined with Partial Least Squares in High Dimensional Imaging Genetics Studies

2013 ◽  
pp. 147-158 ◽  
Author(s):  
Edith Le Floch ◽  
Laura Trinchera ◽  
Vincent Guillemot ◽  
Arthur Tenenhaus ◽  
Jean-Baptiste Poline ◽  
...  
Keyword(s):  
Dimension Reduction ◽  
Least Squares ◽  
Partial Least Squares ◽  
Imaging Genetics ◽  
High Dimensional

scholarly journals Dimension Reduction and Clustering of High Dimensional Data using Auto-Associative Neural Networks

2013 ◽  
Vol 72 (11) ◽  
pp. 31-37 ◽  
Author(s):  
Zalhan MohdZin ◽  
Rubiyah Yusof ◽  
Ehsan Mesbahi
Keyword(s):  
Neural Networks ◽  
Dimension Reduction ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals (Auto)Biographical reflections on the contributions of William F. Loomis (1940-2016) to Dictyostelium biology

2019 ◽  
Vol 63 (8-9-10) ◽  
pp. 343-357
Author(s):  
Adam Kuspa ◽  
Gad Shaulsky
Keyword(s):  
Cell Differentiation ◽  
Molecular Biology ◽  
Genetic Control ◽  
Dictyostelium Discoideum ◽  
University Of California ◽  
High Dimensional ◽  
Social Amoeba ◽  
The Social ◽  
The University ◽  
High Dimensional Datasets

William Farnsworth Loomis studied the social amoeba Dictyostelium discoideum for more than fifty years as a professor of biology at the University of California, San Diego, USA. This biographical reflection describes Dr. Loomis’ major scientific contributions to the field within a career arc that spanned the early days of molecular biology up to the present day where the acquisition of high-dimensional datasets drive research. Dr. Loomis explored the genetic control of social amoeba development, delineated mechanisms of cell differentiation, and significantly advanced genetic and genomic technology for the field. The details of Dr. Loomis’ multifaceted career are drawn from his published work, from an autobiographical essay that he wrote near the end of his career and from extensive conversations between him and the two authors, many of which took place on the deck of his beachfront home in Del Mar, California.

scholarly journals Probing Multiscale Disorder in Pyrochlore and Related Complex Oxides in the Transmission Electron Microscope: A Review

2021 ◽  
Vol 9 ◽  
Author(s):  
Jenna L. Wardini ◽  
Hasti Vahidi ◽  
Huiming Guo ◽  
William J. Bowman
Keyword(s):  
Complex Oxides ◽  
Atomic Scale ◽  
High Dimensional ◽  
Detection Systems ◽  
Spatially Resolved ◽  
Chemical Ordering ◽  
Transmission Electron ◽  
The Many ◽  
High Dimensional Datasets

Transmission electron microscopy (TEM), and its counterpart, scanning TEM (STEM), are powerful materials characterization tools capable of probing crystal structure, composition, charge distribution, electronic structure, and bonding down to the atomic scale. Recent (S)TEM instrumentation developments such as electron beam aberration-correction as well as faster and more efficient signal detection systems have given rise to new and more powerful experimental methods, some of which (e.g., 4D-STEM, spectrum-imaging, in situ/operando (S)TEM)) facilitate the capture of high-dimensional datasets that contain spatially-resolved structural, spectroscopic, time- and/or stimulus-dependent information across the sub-angstrom to several micrometer length scale. Thus, through the variety of analysis methods available in the modern (S)TEM and its continual development towards high-dimensional data capture, it is well-suited to the challenge of characterizing isometric mixed-metal oxides such as pyrochlores, fluorites, and other complex oxides that reside on a continuum of chemical and spatial ordering. In this review, we present a suite of imaging and diffraction (S)TEM techniques that are uniquely suited to probe the many types, length-scales, and degrees of disorder in complex oxides, with a focus on disorder common to pyrochlores, fluorites and the expansive library of intermediate structures they may adopt. The application of these techniques to various complex oxides will be reviewed to demonstrate their capabilities and limitations in resolving the continuum of structural and chemical ordering in these systems.

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