Joint Sequence Complexity Analysis: Application to Social Networks Information Flow

Milioris, Dimitrios; Jacquet, Philippe

doi:10.1002/bltj.21647

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…In a prior work, we extend JC estimate to Markov sources of any order on a finite alphabet. Markov models are more realistic and have a better approximation for text generation than memoryless sources [1], [2]. We derived a second order asymptotics for JC of the following form…”

Section: Joint Complexitymentioning

confidence: 99%

Classification in Twitter via Compressive Sensing

Milioris

2015

2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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In this paper we introduce a novel low dimensional method to perform topic detection and classification in Twitter. The proposed method first employs Joint Complexity to perform topic detection. Then, based on the nature of the data, we apply the theory of Compressive Sensing to perform topic classification by recovering an indicator vector, while reducing significantly the amount of information from tweets. In this paper we exploit datasets in various languages collected by using the Twitter streaming API, and achieve increased classification accuracy when comparing to state-of-the-art methods based on bag-ofwords, along with several reconstruction techniques.

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Section: Joint Complexitymentioning

confidence: 99%

Classification in Twitter via Compressive Sensing

Milioris

2015

2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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“…In a prior work, we extend JC estimate to Markov sources of any order on a finite alphabet. Markov models are more realistic and have a better approximation for text generation than memoryless sources [5], [6]. We derived a second order asymptotics for JC of the following form…”

Section: Joint Complexitymentioning

confidence: 99%

Topic detection and compressed classification in Twitter

Milioris

Jacquet

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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In this paper we introduce a novel information propagation method in Twitter, while maintaining a low computational complexity. It exploits the power of Compressive Sensing in conjunction with a Kalman filter to update the states of a dynamical system. The proposed method first employs Joint Complexity, which is defined as the cardinality of a set of all distinct factors of a given string represented by suffix trees, to perform topic detection. Then based on the inherent spatial sparsity of the data, we apply the theory of Compressive Sensing to perform sparsity-based topic classification by recovering an indicator vector, while reducing significantly the amount of information from tweets, possessing limited power, storage, and processing capabilities, to a central server. We exploit datasets in various languages collected by using the Twitter streaming API and achieve better classification accuracy when compared with state-of-the-art methods.

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“…Besides structured data types mentioned above, unstructured data, such as text, audio, image, and video, are even more commonly encountered [10]. It is not straightforward to regard unstructured data as in a simple vector form, because of its sequential or geometrical properties [28,21,26,27,11]. One needs to carefully transform such unstructured data into a vector representation before applying similarity measures for reasonable success.…”

Section: Introductionmentioning

confidence: 99%

A New Family of Near-metrics for Universal Similarity

Wang¹,

Saniee²,

Kennedy³

et al. 2017

Preprint

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We propose a family of near-metrics based on local graph diffusion to capture similarity for a wide class of data sets. These quasi-metametrics, as their name suggests, dispense with one or two standard axioms of metric spaces, specifically distinguishability and symmetry, so that similarity between data points of arbitrary type and form could be measured broadly and effectively. The proposed near-metric family includes the forward k-step diffusion and its reverse, typically on the graph consisting of data objects and their features. By construction, this family of near-metrics is particularly appropriate for categorical data, continuous data, and vector representations of images and text extracted via deep learning approaches. We conduct extensive experiments to evaluate the performance of this family of similarity measures and compare and contrast with traditional measures of similarity used for each specific application and with the ground truth when available. We show that for structured data including categorical and continuous data, the near-metrics corresponding to normalized forward k-step diffusion (k small) work as one of the best performing similarity measures; for vector representations of text and images including those extracted from deep learning, the near-metrics derived from normalized and reverse k-step graph diffusion (k very small) exhibit outstanding ability to distinguish data points from different classes.

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Joint Sequence Complexity Analysis: Application to Social Networks Information Flow

Cited by 9 publications

References 17 publications

Classification in Twitter via Compressive Sensing

Classification in Twitter via Compressive Sensing

Topic detection and compressed classification in Twitter

A New Family of Near-metrics for Universal Similarity

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