Network Maximal Correlation

Feizi, Soheil; Makhdoumi, Ali; Duffy, Ken R.; Kellis, M; Médard, Muriel

doi:10.1109/tnse.2017.2716966

Cited by 12 publications

(13 citation statements)

References 68 publications

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“…In the preliminary experiment stage, we measured the consistency of labeled data marked by different labelers using Cohen’s Kappa coefficient (Feizi, Makhdoumi, Duffy, Kellis, & Médard, 2017; Lee, Chang, & Chang, 2016; Viera & Garrett, 2005), a statistic that measures the interrater agreement for qualitative (categorical) items; it is used to test, for example, whether two doctors offer consistent diagnoses of the same patient. Its value of 1 implies perfect agreement, and values below 1 imply less than perfect agreement.…”

Section: Resultsmentioning

confidence: 99%

Data-driven Online Learning Engagement Detection via Facial Expression and Mouse Behavior Recognition Technology

Zhang

Liu

et al. 2019

Journal of Educational Computing Research

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Online learning engagement detection is a fundamental problem in educational information technology. Efficient detection of students’ learning situations can provide information to teachers to help them identify students having trouble in real time. To improve the accuracy of learning engagement detection, we have collected two aspects of students’ behavior data: face data (using adaptive weighted Local Gray Code Patterns for facial expression recognition) and mouse interaction. In this article, we propose a novel learning engagement detection algorithm based on the collected data (students’ behavior), which come from the cameras and the mouse in the online learning environment. The cameras were utilized to capture students’ face images, while the mouse movement data were captured simultaneously. In the process of image data labeling, we built two datasets for classifier training and testing. One took the mouse movement data as a reference, while the other did not. We performed experiments on two datasets using several methods and found that the classifier trained by the former dataset had a better performance, and its recognition rate is higher than that of the latter one (94.60% vs. 91.51%).

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Section: Resultsmentioning

confidence: 99%

Data-driven Online Learning Engagement Detection via Facial Expression and Mouse Behavior Recognition Technology

Zhang

Liu

et al. 2019

Journal of Educational Computing Research

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“…The y-axis represents the inference error on a scale of 1. The error function is adopted from Section 6.1 of Feizi et al (2017). This error directly relates to the error in estimating cyber-risk.…”

Section: Resultsmentioning

confidence: 99%

On Robust Estimates of Correlated Risk in Cyber-Insured IT Firms

Pal

Golubchik

Psounis

et al. 2019

ACM Trans. Manage. Inf. Syst.

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In this article, we comment on the drawbacks of the existing AI-based Bayesian network (BN) cybervulnerability analysis (C-VA) model proposed in Mukhopadhyay et al. (2013) to assess cyber-risk in IT firms, where this quantity is usually a joint distribution of multiple risk (random) variables (e.g., quality of antivirus, frequency of monitoring, etc.) coming from heterogeneous distribution families. As a major modeling drawback, Mukhopadhyay et al. (2013) assume that any pair of random variables in the BN are linearly correlated with each other. This simplistic assumption might not always hold true for general IT organizational environments. Thus, the use of the C-VA model in general will result in loose estimates of correlated IT risk and will subsequently affect cyber-insurance companies in framing profitable coverage policies for IT organizations. To this end, we propose methods to (1) find a closed-form expression for the maximal correlation arising between pairs of discrete random variables, whose value finds importance in getting robust estimates of copula-induced computations of organizational cyber-risk, and (2) arrive at a computationally effective mechanism to compute nonlinear correlations among pairs of discrete random variables in the correlation matrix of the CBBN model (Mukhopadhyay et al. 2013). We also prove that an empirical computation of MC using our method converges rapidly, that is, exponentially fast, to the true correlation value in the number of samples. Our proposed method contributes to a tighter estimate of IT cyber-risk under environments of low-risk data availability and will enable insurers to better assess organizational risks and subsequently underwrite profitable cyber-insurance policies. CCS Concepts: • Mathematics of computing → Probability and statistics; Multivariate statistics; • Security and privacy → Human and societal aspects of security and privacy; Economics of security and privacy;

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“…Note that there are some other generalizations to maximal correlations to multiple random variables. For example, the network maximal correlation (NMC) proposed in [18] defined a correlation measurement in the same way as (21) but with a slightly different constraint:…”

Section: A the Hgr Maximal Correlationmentioning

confidence: 99%

An Information-Theoretic Approach to Unsupervised Feature Selection for High-Dimensional Data

Huang

Zheng

2020

IEEE J. Sel. Areas Inf. Theory

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In this paper, we propose an information-theoretic approach to design the functional representations to extract the hidden common structure shared by a set of random variables. The main idea is to measure the common information between the random variables by Watanabe's total correlation, and then find the hidden attributes of these random variables such that the common information is reduced the most given these attributes. We show that these attributes can be characterized by an exponential family specified by the eigen-decomposition of some pairwise joint distribution matrix. Then, we adopt the log-likelihood functions for estimating these attributes as the desired functional representations of the random variables, and show that such representations are informative to describe the common structure. Moreover, we design both the multivariate alternating conditional expectation (MACE) algorithm to compute the proposed functional representations for discrete data, and a novel neural network training approach for continuous or high-dimensional data. Furthermore, we show that our approach has deep connections to existing techniques, such as Hirschfeld-Gebelein-Rényi (HGR) maximal correlation, linear principal component analysis (PCA), and consistent functional map, which establishes insightful connections between information theory and machine learning. Finally, the performances of our algorithms are validated by numerical simulations.S.-L. Huang is with the 1 Specifically, for random variables X1, . . . , X d , the total correlation is defined as the Kullback-Leibler (K-L) divergence D(PX 1 ···X d PX 1 · · · PX d ) between the joint distribution and the product of the marginal distributions.2 Note that I(U ; X d ) measures the amount of information of U about the whole X d , while L(X d |U ) measure the amount of information only about the common structure. The constraint I(U ; X d ) ≤ δ allows us to focus on low-dimensional attribute of W , in which we typically choose δ to be small.

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Network Maximal Correlation

Cited by 12 publications

References 68 publications

Data-driven Online Learning Engagement Detection via Facial Expression and Mouse Behavior Recognition Technology

Data-driven Online Learning Engagement Detection via Facial Expression and Mouse Behavior Recognition Technology

On Robust Estimates of Correlated Risk in Cyber-Insured IT Firms

An Information-Theoretic Approach to Unsupervised Feature Selection for High-Dimensional Data

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