Abstract
This article presents a new paradigm of Artificial Neural Networks (ANNs): the Auto-Contractive Maps (Auto- CM). The Auto-CM differ from the traditional ANNs under many viewpoints: the Auto-CM start their learning task without a random initialization of their weights, they meet their convergence criterion when all their output nodes become null, their weights matrix develops a data driven warping of the original Euclidean space, they show suitable topological properties, etc. Further two new algorithms, theoretically linked to Auto-CM are presented: the first one is useful to evaluate the complexity and the topological information of any kind of connected graph: the H Function is the index to measure the global hubness of the graph generated by the Auto-CM weights matrix. The second one is named Maximally Regular Graph (MRG) and it is an development of the traditionally Minimum Spanning Tree (MST). Finally, Auto-CM and MRG, with the support of the H Function, are applied to a real complex dataset about Alzheimer disease: this data come from the very known Nuns Study, where variables measuring the abilities of normal and Alzheimer subject during their lifespan and variables measuring the number of the plaques and of the tangles in their brain after their death. The example of the Alzheimer data base is extremely useful to figure out how this new approach can help to re design bottom-up the overall structure of factors related to a complex disease like this.
Keywords: Artificial neural networks, contractive maps, artificial adaptive systems, theory of graph, minimum spanning tree, Alzheimer disease, nun study
Current Alzheimer Research
Title: Auto-Contractive Maps: An Artificial Adaptive System for Data Mining. An Application to Alzheimer Disease
Volume: 5 Issue: 5
Author(s): Massimo Buscema, Enzo Grossi, Dave Snowdon and Piero Antuono
Affiliation:
Keywords: Artificial neural networks, contractive maps, artificial adaptive systems, theory of graph, minimum spanning tree, Alzheimer disease, nun study
Abstract: This article presents a new paradigm of Artificial Neural Networks (ANNs): the Auto-Contractive Maps (Auto- CM). The Auto-CM differ from the traditional ANNs under many viewpoints: the Auto-CM start their learning task without a random initialization of their weights, they meet their convergence criterion when all their output nodes become null, their weights matrix develops a data driven warping of the original Euclidean space, they show suitable topological properties, etc. Further two new algorithms, theoretically linked to Auto-CM are presented: the first one is useful to evaluate the complexity and the topological information of any kind of connected graph: the H Function is the index to measure the global hubness of the graph generated by the Auto-CM weights matrix. The second one is named Maximally Regular Graph (MRG) and it is an development of the traditionally Minimum Spanning Tree (MST). Finally, Auto-CM and MRG, with the support of the H Function, are applied to a real complex dataset about Alzheimer disease: this data come from the very known Nuns Study, where variables measuring the abilities of normal and Alzheimer subject during their lifespan and variables measuring the number of the plaques and of the tangles in their brain after their death. The example of the Alzheimer data base is extremely useful to figure out how this new approach can help to re design bottom-up the overall structure of factors related to a complex disease like this.
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Cite this article as:
Buscema Massimo, Grossi Enzo, Snowdon Dave and Antuono Piero, Auto-Contractive Maps: An Artificial Adaptive System for Data Mining. An Application to Alzheimer Disease, Current Alzheimer Research 2008; 5 (5) . https://dx.doi.org/10.2174/156720508785908928
DOI https://dx.doi.org/10.2174/156720508785908928 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
Call for Papers in Thematic Issues
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Aims and Scope: Introduction: Alzheimer's disease (AD) poses a significant global health challenge, with an increasing prevalence that demands concerted efforts to advance our understanding and strategies for prevention, diagnosis, treatment, and rehabilitation. This thematic issue aims to bring together cutting-edge research and innovative approaches from multidisciplinary perspectives to address ...read more
Alzheimer's Disease Drug Development
Alzheimer's disease is a progressive neurodegenerative disorder that affects millions of people worldwide. Despite decades of research, no cure or disease-modifying treatment is available yet. Therefore, the need for developing effective therapies to treat Alzheimer's disease is an urgent matter. This special issue aims to provide a comprehensive overview of ...read more
Current updates on the Role of Neuroinflammation in Neurodegenerative Disorders
Neuroinflammation is an invariable hallmark of chronic and acute neurodegenerative disorders and has long been considered a potential drug target for Alzheimer?s disease (AD) and dementia. Significant evidence of inflammatory processes as a feature of AD is provided by the presence of inflammatory markers in plasma, CSF and postmortem brain ...read more
Deep Learning for Advancing Alzheimer's Disease Research
Alzheimer's disease (AD) poses a significant global health challenge, with an increasing number of individuals affected yearly. Deep learning, a subfield of artificial intelligence, has shown immense potential in various domains, including healthcare. This thematic issue of Current Alzheimer Research explores the application of deep learning techniques in advancing our ...read more
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