scholarly journals Self-organizing mappings on the flag manifold with applications to hyper-spectral image data analysis

2021 ◽  
Author(s):  
Xiaofeng Ma ◽  
Michael Kirby ◽  
Chris Peterson
Keyword(s):  
Data Analysis ◽  
Image Data ◽  
Building Blocks ◽  
Flag Manifold ◽  
Vector Spaces ◽  
Mathematical Framework ◽  
Basic Algorithm ◽  
Geometric Framework ◽  
Fixed Type ◽  
Self Organizing

AbstractA flag is a nested sequence of vector spaces. The type of the flag encodes the sequence of dimensions of the vector spaces making up the flag. A flag manifold is a manifold whose points parameterize all flags of a fixed type in a fixed vector space. This paper provides the mathematical framework necessary for implementing self-organizing mappings on flag manifolds. Flags arise implicitly in many data analysis contexts including wavelet, Fourier, and singular value decompositions. The proposed geometric framework in this paper enables the computation of distances between flags, the computation of geodesics between flags, and the ability to move one flag a prescribed distance in the direction of another flag. Using these operations as building blocks, we implement the SOM algorithm on a flag manifold. The basic algorithm is applied to the problem of parameterizing a set of flags of a fixed type.

scholarly journals Provision of an integrated data analysis platform for computational neuroscience experiments

2014 ◽  
Vol 16 (3) ◽  
pp. 150-169 ◽  
Author(s):  
Kamran Munir ◽  
Saad Liaquat Kiani ◽  
Khawar Hasham ◽  
Richard McClatchey ◽  
Andrew Branson ◽  
...  
Keyword(s):  
Data Analysis ◽  
Computational Neuroscience ◽  
Image Data ◽  
Building Blocks ◽  
Study Data ◽  
Scientific Workflow ◽  
Integrated Analysis ◽  
Data Sets ◽  
Biomedical Data ◽  
Content Type

Purpose – The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community. Design/methodology/approach – The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses. Findings – The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases. Originality/value – The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information.

scholarly journals Deep Self-Organizing Map of Convolutional Layers for Clustering and Visualizing Image Data

2021 ◽  
Vol 3 (4) ◽  
pp. 879-899
Author(s):  
Christos Ferles ◽  
Yannis Papanikolaou ◽  
Stylianos P. Savaidis ◽  
Stelios A. Mitilineos
Keyword(s):  
Neural Networks ◽  
Image Data ◽  
Building Blocks ◽  
The Self ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Deep Architecture ◽  
Unsupervised Deep Learning ◽  
Deep Learning Model ◽  
Self Organizing

The self-organizing convolutional map (SOCOM) hybridizes convolutional neural networks, self-organizing maps, and gradient backpropagation optimization into a novel integrated unsupervised deep learning model. SOCOM structurally combines, architecturally stacks, and algorithmically fuses its deep/unsupervised learning components. The higher-level representations produced by its underlying convolutional deep architecture are embedded in its topologically ordered neural map output. The ensuing unsupervised clustering and visualization operations reflect the model’s degree of synergy between its building blocks and synopsize its range of applications. Clustering results are reported on the STL-10 benchmark dataset coupled with the devised neural map visualizations. The series of conducted experiments utilize a deep VGG-based SOCOM model.

Image data analysis and classification in marketing

2012 ◽  
Vol 6 (4) ◽  
pp. 253-276 ◽  
Author(s):  
Daniel Baier ◽  
Ines Daniel ◽  
Sarah Frost ◽  
Robert Naundorf
Keyword(s):  
Data Analysis ◽  
Image Data

scholarly journals Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms

GigaScience ◽  
2017 ◽  
Vol 6 (11) ◽  
Author(s):  
Fernando Perez-Sanz ◽  
Pedro J Navarro ◽  
Marcos Egea-Cortines
Keyword(s):  
Data Analysis ◽  
Image Acquisition ◽  
Image Data ◽  
Plant Phenomics

scholarly journals Geometry of Multiprimary Display Colors II: Metameric Control Sets and Gamut Tiling Color Control Functions

2021 ◽  
Author(s):  
Carlos Rodriguez-Pardo ◽  
Gaurav Sharma
Keyword(s):  
Broad Class ◽  
Convex Polytope ◽  
Building Blocks ◽  
Complete Characterization ◽  
Mathematical Framework ◽  
Control Functions ◽  
Control Sets ◽  
Multiple Alternative ◽  
Color Control

<div>For multiprimary displays that have four or more primaries, a color may be reproduced using multiple alternative control vectors. We provide a complete characterization of the Metameric Control Set (MCS), i.e., the set of control vectors that reproduce a given color on the display. Specifically, we show that MCS is a convex polytope whose vertices are control vectors obtained from (parallelepiped) tilings of the gamut, i.e., the range of colors that the display can produce. The mathematical framework that we develop: (a) characterizes gamut tilings in terms of fundamental building blocks called facet spans, (b) establishes that the vertices of the MCS are fully characterized by the tilings of the gamut, and (c) introduces a methodology for the efficient enumeration of gamut tilings. The framework reveals the fundamental inter-relations between the geometry of the MCS and the geometry of the gamut developed in a companion Part I paper, and provides insight into alternative strategies for color control. Our characterization of tilings and the strategy for their enumeration also advance knowledge in geometry, providing new approaches and computational results for the enumeration of tilings for a broad class of zonotopes in R<sup>3</sup>.</div>

Genetic Data Analysis

2021 ◽  
pp. 358-372
Author(s):  
M. Shamila ◽  
Amit Kumar Tyagi
Keyword(s):  
Data Analysis ◽  
Association Studies ◽  
Single Gene ◽  
Genetic Data ◽  
Building Blocks ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Human Beings ◽  
Genome Wide ◽  
Genetic Data Analysis

Genome-wide association studies (GWAS) or genetic data analysis is used to discover common genetic factors which influence the health of human beings and become a part of a disease. The concept of using genomics has increased in recent years, especially in e-healthcare. Today there is huge improvement required in this field or genomics. Note that the terms genomics and genetics are not similar terms here. Basically, the human genome is made up of DNA, which consists of four different chemical building blocks (called bases and abbreviated A, T, C, and G). Based on this, we differentiate each and every human being living on earth. The term ‘genetics' originated from the Greek word ‘genetikos'. It means ‘origin'. In simple terms, genetics can be defined as a branch of biology, which deals with the study of the functionalities and composition of a single gene in an organism. There are mainly three branches of genetics, which include classical genetics, molecular genetics, and population genetics.

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