TY - GEN

T1 - Diffusion bases dimensionality reduction

AU - Schclar, Alon

AU - Averbuch, Amir

N1 - Publisher Copyright:
Copyright © 2015 by SCITEPRESS - Science and Technology Publications, Lda.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Dimensionality Reduction

KW - Unsupervised Learning

UR - http://www.scopus.com/inward/record.url?scp=84960950430&partnerID=8YFLogxK

U2 - 10.5220/0005625301510156

DO - 10.5220/0005625301510156

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AN - SCOPUS:84960950430

T3 - IJCCI 2015 - Proceedings of the 7th International Joint Conference on Computational Intelligence

SP - 151

EP - 156

BT - NCTA

A2 - Rosa, Agostinho

A2 - Merelo, Juan Julian

A2 - Dourado, Antonio

A2 - Cadenas, Jose M.

A2 - Madani, Kurosh

A2 - Ruano, Antonio

A2 - Filipe, Joaquim

A2 - Filipe, Joaquim

PB - SciTePress

T2 - 7th International Joint Conference on Computational Intelligence, IJCCI 2015

Y2 - 12 November 2015 through 14 November 2015

ER -