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Topic Detection and Classification in Social Networks pp 69–92Cite as

Extension of Joint Complexity and Compressive Sensing

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Abstract

In this chapter, the theory of Joint Complexity and Compressive Sensing has been extended to three research subjects, (a) classification encryption via compressed permuted measurement matrices, (b) dynamic classification completeness based on Matrix Completion and (c) encryption based on the Eulerian circuits of original texts. In the first additional research subject we study the encryption property of Compressive Sensing in order to secure the classification process in Twitter without an extra cryptographic layer. The measurements obtained are considered to be weakly encrypted due to their acquisition process, which was verified by the experimental results. In the second additional research subject we study the application of Matrix Completion (MC) in topic detection and classification. Based on the spatial correlation of tweets and the spatial characteristics of the score matrices, we apply a novel framework which extends the Matrix Completion to build dynamically complete matrices from a small number of random sample Joint Complexity scores. In the third additional research subject, we present an encryption system based on Eulerian circuits , that destructs the semantics of a text while retaining it in correct syntax. We study the performance on Markov models , and perform experiments on real text.

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Notes

  1. 1.

    For the implementation of methods (1)–(5) the MATLAB codes can be found in:http://sparselab.stanford.edu/,http://www.acm.caltech.edu/l1magic,http://people.ee.duke.edu/~lcarin/BCS.html

  2. 2.

    For the implementation of methods (1)–(5) the MATLAB codes can be found in:http://sparselab.stanford.edu/,http://www.acm.caltech.edu/l1magic,http://people.ee.duke.edu/~lcarin/BCS.html

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  1. Massachusetts Institute of Technology, Cambridge, MA, USA

    Dimitrios Milioris

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  1. Dimitrios Milioris
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Milioris, D. (2018). Extension of Joint Complexity and Compressive Sensing. In: Topic Detection and Classification in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-66414-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-66414-9_5

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  • Print ISBN: 978-3-319-66413-2

  • Online ISBN: 978-3-319-66414-9

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