Topic modeling for analysis of big data tensor decompositions

Thomas S. Henretty; M. Harper Langston; Muthu Baskaran; James Ezick; Richard Lethin

doi:10.1117/12.2306933

9 May 2018 Topic modeling for analysis of big data tensor decompositions

Thomas S. Henretty, M. Harper Langston, Muthu Baskaran, James Ezick, Richard Lethin

Proceedings Volume 10652, Disruptive Technologies in Information Sciences; 1065208 (2018) https://doi.org/10.1117/12.2306933
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

Tensor decompositions are a class of algorithms used for unsupervised pattern discovery. Structured, multidimensional datasets are encoded as tensors and decomposed into discrete, coherent patterns captured as weighted collections of high-dimensional vectors known as components. Tensor decompositions have recently shown promising results when addressing problems related to data comprehension and anomaly discovery in cybersecurity and intelligence analysis. However, analysis of Big Data tensor decompositions is currently a critical bottleneck owing to the volume and variety of unlabeled patterns that are produced. We present an approach to automated component clustering and classification based on the Latent Dirichlet Allocation (LDA) topic modeling technique and show example applications to representative cybersecurity and geospatial datasets.

Conference Presentation

Citation Download Citation

Thomas S. Henretty, M. Harper Langston, Muthu Baskaran, James Ezick, and Richard Lethin "Topic modeling for analysis of big data tensor decompositions", Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 1065208 (9 May 2018); https://doi.org/10.1117/12.2306933

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