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Authors: Alon Schclar 1 and Amir Averbuch 2

Affiliations: 1 The Academic College of Tel-Aviv Yaffo, Israel ; 2 Tel-Aviv University, Israel

ISBN: 978-989-758-157-1

Keyword(s): Dimensionality Reduction, Unsupervised Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Theory and Methods

Abstract: The overflow of data is a critical contemporary challenge in many areas such as hyper-spectral sensing, information retrieval, biotechnology, social media mining, classification etc. It is usually manifested by a high-dimensional representation of data observations. In most cases, the information that is inherent in highdimensional datasets is conveyed by a small number of parameters that correspond to the actual degrees of freedom of the dataset. In order to efficiently process the dataset, one needs to derive these parameters by embedding the dataset into a low-dimensional space. This process is commonly referred to as dimensionality reduction or feature extraction. We present a novel algorithm for dimensionality reduction – diffusion bases – which explores the connectivity among the coordinates of the data and is dual to the diffusion maps algorithm. The algorithm reduces the dimensionality of the data while maintaining the coherency of the information that is conveyed by t he data. (More)

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Paper citation in several formats:
Schclar, A. and Averbuch, A. (2015). Diffusion Bases Dimensionality Reduction.In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3 NCTA: NCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 151-156. DOI: 10.5220/0005625301510156

@conference{ncta15,
author={Alon Schclar. and Amir Averbuch.},
title={Diffusion Bases Dimensionality Reduction},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3 NCTA: NCTA, (ECTA 2015)},
year={2015},
pages={151-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005625301510156},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3 NCTA: NCTA, (ECTA 2015)
TI - Diffusion Bases Dimensionality Reduction
SN - 978-989-758-157-1
AU - Schclar, A.
AU - Averbuch, A.
PY - 2015
SP - 151
EP - 156
DO - 10.5220/0005625301510156

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