A New Algorithm using Independent Components for Classification and Prediction of High Dimensional Data

Subhajit Chakrabarty, Haim Levkowitz

2020

Abstract

Dimensionality reduction of high-dimensional data is often desirable, in particular where data analysis includes visualization – an ever more common scenario nowadays. Principal Component Analysis, and more recently Independent Component Analysis (ICA) are among the most common approaches. ICA may output components that are redundant. Interpretation of such groups of independent components may be achieved through application to tasks such as classification, regression, and visualization. One major problem is that grouping of independent components for high-dimensional time series is difficult. Our objective is to provide a comparative analysis using independent components for given grouping and prediction tasks related to high-dimensional time series. Our contribution is that we have developed a novel semi-supervised procedure for classification. This also provides consistency to the overall ICA result. We have conducted a comparative performance analysis for classification and prediction tasks on time series. This research has a broader impact on all kinds of ICA applied in several domains, including bio-medical sensors (such as electroencephalogram), astronomy, financial time series, environment and remote sensing.

Paper Citation

in Harvard Style

Chakrabarty S. and Levkowitz H. (2020). A New Algorithm using Independent Components for Classification and Prediction of High Dimensional Data. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 3: IVAPP; ISBN 978-989-758-402-2, SciTePress, pages 265-272. DOI: 10.5220/0009148602650272

in Bibtex Style

@conference{ivapp20,
author={Subhajit Chakrabarty and Haim Levkowitz},
title={A New Algorithm using Independent Components for Classification and Prediction of High Dimensional Data},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 3: IVAPP},
year={2020},
pages={265-272},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009148602650272},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 3: IVAPP
TI - A New Algorithm using Independent Components for Classification and Prediction of High Dimensional Data
SN - 978-989-758-402-2
AU - Chakrabarty S.
AU - Levkowitz H.
PY - 2020
SP - 265
EP - 272
DO - 10.5220/0009148602650272
PB - SciTePress