Diffusion Bases Dimensionality Reduction

Alon Schclar, Amir Averbuch


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 the data.


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Paper Citation

in Harvard Style

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

in Bibtex Style

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, (ECTA 2015)},

in EndNote Style

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: 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