# Network-based Dimensionality Reduction and Analysis (NDA)

Version 0.1.6 (18.4 MB) by
Network-based dimensionality reduction and analysis in MATLAB
Updated 23 Mar 2023

# nda_matlab

Network-based dimensionality reduction and analysis in MATLAB

This package provides Network-based dimensionality reduction and analysis.

• Network-based dimensionality reduction and analysis.

• Dimensional reduction.

• Plot and biplot functions

• Data generation

#### Author

• Zsolt T. Kosztyan

#### Contributor

• Zsolt T. Kosztyan

#### Maintainer

• Zsolt T. Kosztyan

### Outputs:

Scores: n by m matrix of factor scores, where n is the number of rows in a datasource, m is tne number of latent factors CMTX: m by m factor correlation matrix COMMUNALITY: n by 1 row vector of communalities LOADINGS: s by m matrix of factor loadings, where s is the number of selected indicators LTABLE: s by m table of factor loadings, where s is the number of % selected indicators MEMBERSHIPS: m by 1 vector of membership

### Input:

data: n by M matrix/table/structure of data source (mandatory)

### Optional input parameters:

XHeader: M by 1 cell array of variable names CorrMethod|cor_method: Correlation method (optional) Pearson|pearson|'1'|1: Pearson's correlation (default) Spearman|spearman|'2'|2: Spearman's correlation Kendall|kendall|'3'|3: Kendall's correlation Distance|distance|'4'|4: Distance correlation -otherwise: 1 (Pearson's correlation) MinCor2|min_R: Minimal square correlation between indicators (default: 0) MinimalCommunity|min_comm: Minimal number of indicators in a community (default: 2) Gamma: Gamma parameter in multiresolution null_modell (default: 1) NullModelType|null_model_type (default: 1); NewmannGrivan|'1'|1: Newmann-Grivan's null modell AvgDet: Null model is the mean of square correlations between indicators MinDet,min_det: Null modell is the specified minimal square correlation (min_det) MinEigCentValue|min_evalue: Minimal EVC value (default: 0.00) MinCommunality|min_communality: Minimal communality value of indicators (default: 0.25) ComCommunalities|com_communalities=0.0: Minimal common communalities RotateMethod: Rotation method (default: none); Biplots: Draw biplots (default: false) cuts: Draw correlation graph with cuts value (default: 0 => No correlation graph)

## Requirements:

Eigenvector centralities (if Matlab release is older than R2020a) (Contributors): Xi-Nian Zuo, Chinese Academy of Sciences, 2010 Rick Betzel, Indiana University, 2012 Mika Rubinov, University of Cambridge, 2015

Modified GenLouvain toolbox (Contributurs): Lucas G. S. Jeub, Marya Bazzi, Inderjit S. Jutla, and Peter J. Mucha, "A generalized Louvain method for community detection implemented in MATLAB," https://github.com/GenLouvain/GenLouvain (2011-2019).

### Cite As

Kosztyán, Zsolt Tibor (2024). Network-based Dimensionality Reduction and Analysis (NDA) (https://github.com/kzst/nda_matlab/releases/tag/0.1.6), GitHub. Retrieved .

Kosztyán, Zsolt T., et al. “Network-Based Dimensionality Reduction of High-Dimensional, Low-Sample-Size Datasets.” Knowledge-Based Systems, vol. 251, Elsevier BV, Sept. 2022, p. 109180, doi:10.1016/j.knosys.2022.109180.

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##### MATLAB Release Compatibility
Created with R2022a
Compatible with any release
##### Platform Compatibility
Windows macOS Linux

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Version Published Release Notes
0.1.6

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.