scholarly journals A novel approach to understanding Parkinsonian cognitive decline using minimum spanning trees, edge cutting, and magnetoencephalography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Olivier B. Simon ◽  
Isabelle Buard ◽  
Donald C. Rojas ◽  
Samantha K. Holden ◽  
Benzi M. Kluger ◽  
...  
Keyword(s):  
Gene Expression Analysis ◽  
Spanning Trees ◽  
Minimum Spanning Tree ◽  
High Dimensional Data ◽  
Null Distribution ◽  
High Dimensional ◽  
Neural Connectivity ◽  
Cross Sectional ◽  
Novel Approach ◽  
Wide Range

AbstractGraph theory-based approaches are efficient tools for detecting clustering and group-wise differences in high-dimensional data across a wide range of fields, such as gene expression analysis and neural connectivity. Here, we examine data from a cross-sectional, resting-state magnetoencephalography study of 89 Parkinson’s disease patients, and use minimum-spanning tree (MST) methods to relate severity of Parkinsonian cognitive impairment to neural connectivity changes. In particular, we implement the two-sample multivariate-runs test of Friedman and Rafsky (Ann Stat 7(4):697–717, 1979) and find it to be a powerful paradigm for distinguishing highly significant deviations from the null distribution in high-dimensional data. We also generalize this test for use with greater than two classes, and show its ability to localize significance to particular sub-classes. We observe multiple indications of altered connectivity in Parkinsonian dementia that may be of future use in diagnosis and prediction.

scholarly journals Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Trees

2019 ◽  
Author(s):  
Daniel Probst ◽  
Jean-Louis Reymond
Keyword(s):  
Data Visualization ◽  
Particle Physics ◽  
Cancer Biology ◽  
Spanning Trees ◽  
Minimum Spanning Tree ◽  
High Dimensional Data ◽  
Locality Sensitive Hashing ◽  
High Dimensional ◽  
Data Sets ◽  
Data Set

<div>Here, we introduce a new data visualization and exploration method, TMAP (tree-map), which exploits locality sensitive hashing, Kruskal’s minimum-spanning-tree algorithm, and a multilevel multipole-based graph layout algorithm to represent large and high dimensional data sets as a tree structure, which is readily understandable and explorable. Compared to other data visualization methods such as t-SNE or UMAP, TMAP increases the size of data sets that can be visualized due to its significantly lower memory requirements and running time and should find broad applicability in the age of big data. We exemplify TMAP in the area of cheminformatics with interactive maps for 1.16 million drug-like molecules from ChEMBL, 10.1 million small molecule fragments from FDB17, and 131 thousand 3D-structures of biomolecules from the PDB Databank, and to visualize data from literature (GUTENBERG data set), cancer biology (PANSCAN data set) and particle physics (MiniBooNE data set). TMAP is available as a Python package. Installation, usage instructions and application examples can be found at http://tmap.gdb.tools.</div>

A Novel Approach to Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE

2021 ◽  
pp. 89-108
Author(s):  
Mujtaba Husnain ◽  
Malik Muhammad Saad Missen ◽  
Shahzad Mumtaz ◽  
Muhammad Muzzamil Luqman ◽  
Mickael Coustaty ◽  
...  
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Novel Approach

scholarly journals Detecting Outliers in High Dimensional Data Sets using Z-Score Methodology

2019 ◽  
Vol 9 (1) ◽  
pp. 48-53
Keyword(s):  
Outlier Detection ◽  
Credit Card ◽  
High Dimensional Data ◽  
Research Area ◽  
High Dimensional ◽  
Data Sets ◽  
Z Score ◽  
Wide Range ◽  
Efficiency And Effectiveness ◽  
Projected Methods

Outlier detection is an interesting research area in machine learning. With the recently emergent tools and varied applications, the attention of outlier recognition is growing significantly. Recently, a significant number of outlier detection approaches have been observed and effectively applied in a wide range of fields, comprising medical health, credit card fraud and intrusion detection. They can be utilized for conservative data analysis. However, Outlier recognition aims to discover sequence in data that do not conform to estimated performance. In this paper, we presented a statistical approach called Z-score method for outlier recognition in high-dimensional data. Z-scores is a novel method for deciding distant data based on data positions on charts. The projected method is computationally fast and robust to outliers’ recognition. A comparative Analysis with extant methods is implemented with high dimensional datasets. Exploratory outcomes determines an enhanced accomplishment, efficiency and effectiveness of our projected methods.

scholarly journals Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 107 ◽  
Author(s):  
Mujtaba Husnain ◽  
Malik Missen ◽  
Shahzad Mumtaz ◽  
Muhammad Luqman ◽  
Mickaël Coustaty ◽  
...  
Keyword(s):  
Local Structure ◽  
High Dimensional Data ◽  
Three Dimensional ◽  
Principal Component ◽  
Large Data ◽  
High Dimensional ◽  
Data Set ◽  
Novel Approach ◽  
Critical Issues ◽  
Low Dimensional

We applied t-distributed stochastic neighbor embedding (t-SNE) to visualize Urdu handwritten numerals (or digits). The data set used consists of 28 × 28 images of handwritten Urdu numerals. The data set was created by inviting authors from different categories of native Urdu speakers. One of the challenging and critical issues for the correct visualization of Urdu numerals is shape similarity between some of the digits. This issue was resolved using t-SNE, by exploiting local and global structures of the large data set at different scales. The global structure consists of geometrical features and local structure is the pixel-based information for each class of Urdu digits. We introduce a novel approach that allows the fusion of these two independent spaces using Euclidean pairwise distances in a highly organized and principled way. The fusion matrix embedded with t-SNE helps to locate each data point in a two (or three-) dimensional map in a very different way. Furthermore, our proposed approach focuses on preserving the local structure of the high-dimensional data while mapping to a low-dimensional plane. The visualizations produced by t-SNE outperformed other classical techniques like principal component analysis (PCA) and auto-encoders (AE) on our handwritten Urdu numeral dataset.

scholarly journals A Robust Supervised Variable Selection for Noisy High-Dimensional Data

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jan Kalina ◽  
Anna Schlenker
Keyword(s):  
Variable Selection ◽  
Dimensionality Reduction ◽  
Robust Statistics ◽  
High Dimensional Data ◽  
Real Data ◽  
High Dimensional ◽  
Adaptive Weights ◽  
Novel Approach ◽  
Reduction Methods ◽  
Data Adaptive

The Minimum Redundancy Maximum Relevance (MRMR) approach to supervised variable selection represents a successful methodology for dimensionality reduction, which is suitable for high-dimensional data observed in two or more different groups. Various available versions of the MRMR approach have been designed to search for variables with the largest relevance for a classification task while controlling for redundancy of the selected set of variables. However, usual relevance and redundancy criteria have the disadvantages of being too sensitive to the presence of outlying measurements and/or being inefficient. We propose a novel approach called Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR), suitable for noisy high-dimensional data observed in two groups. It combines principles of regularization and robust statistics. Particularly, redundancy is measured by a new regularized version of the coefficient of multiple correlation and relevance is measured by a highly robust correlation coefficient based on the least weighted squares regression with data-adaptive weights. We compare various dimensionality reduction methods on three real data sets. To investigate the influence of noise or outliers on the data, we perform the computations also for data artificially contaminated by severe noise of various forms. The experimental results confirm the robustness of the method with respect to outliers.

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