COMPUTING PERSISTENT FEATURES IN BIG DATA: A DISTRIBUTED DIMENSION REDUCTION APPROACH

被引：0

作者：

Wilkerson, Adam C. ^{[1
]}

Chintakunta, Harish ^{[1
]}

Krim, Hamid ^{[1
]}

机构：

[1] N Carolina State Univ, Raleigh, NC 27695 USA

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2014年

关键词：

Simplicial complex; persistent homology; simplicial collapse; topological data analysis; strong collapse; SENSOR NETWORKS; TOPOLOGY; COVERAGE;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Persistent homology has become one of the most popular tools used in topological data analysis for analyzing big data sets. In an effort to minimize the computational complexity of finding the persistent homology of a data set, we develop a simplicial collapse algorithm called the selective collapse. This algorithm works by representing the previously developed strong collapse as a forest and uses that forest data to improve the speed of both the strong collapse and of persistent homology. Finally, we demonstrate the savings in computational complexity using geometric random graphs.

引用

页数：5

共 24 条

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[2]

[Anonymous], 2001, ALGEBRAIC TOPOLOGY

[3]

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