Parallel Framework for Dimensionality Reduction of Large-Scale Datasets
Joint Authors
Samudrala, Sai Kiranmayee
Zola, Jaroslaw
Aluru, Srinivas
Ganapathysubramanian, Baskar
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-03-10
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Dimensionality reduction refers to a set of mathematical techniques used to reduce complexity of the original high-dimensional data, while preserving its selected properties.
Improvements in simulation strategies and experimental data collection methods are resulting in a deluge of heterogeneous and high-dimensional data, which often makes dimensionality reduction the only viable way to gain qualitative and quantitative understanding of the data.
However, existing dimensionality reduction software often does not scale to datasets arising in real-life applications, which may consist of thousands of points with millions of dimensions.
In this paper, we propose a parallel framework for dimensionality reduction of large-scale data.
We identify key components underlying the spectral dimensionality reduction techniques, and propose their efficient parallel implementation.
We show that the resulting framework can be used to process datasets consisting of millions of points when executed on a 16,000-core cluster, which is beyond the reach of currently available methods.
To further demonstrate applicability of our framework we perform dimensionality reduction of 75,000 images representing morphology evolution during manufacturing of organic solar cells in order to identify how processing parameters affect morphology evolution.
American Psychological Association (APA)
Samudrala, Sai Kiranmayee& Zola, Jaroslaw& Aluru, Srinivas& Ganapathysubramanian, Baskar. 2015. Parallel Framework for Dimensionality Reduction of Large-Scale Datasets. Scientific Programming،Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1076502
Modern Language Association (MLA)
Samudrala, Sai Kiranmayee…[et al.]. Parallel Framework for Dimensionality Reduction of Large-Scale Datasets. Scientific Programming No. 2015 (2015), pp.1-12.
https://search.emarefa.net/detail/BIM-1076502
American Medical Association (AMA)
Samudrala, Sai Kiranmayee& Zola, Jaroslaw& Aluru, Srinivas& Ganapathysubramanian, Baskar. Parallel Framework for Dimensionality Reduction of Large-Scale Datasets. Scientific Programming. 2015. Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1076502
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1076502