The Experts below are selected from a list of 8718 Experts worldwide ranked by ideXlab platform
Jilles Vreeken - One of the best experts on this subject based on the ideXlab platform.
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discovering reliable correlations in categorical data
International Conference on Data Mining, 2019Co-Authors: Panagiotis Mandros, Mario Boley, Jilles VreekenAbstract:In many scientific tasks we are interested in finding correlations in our data. This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of Attributes, how to do so without having to make assumptions on data distribution or the type of correlation, and, how to search efficiently for the most Correlated Attribute sets. We answer these questions for discovery tasks with categorical data. In particular, we propose a corrected-for-chance, consistent, and efficient estimator for normalized total correlation, in order to obtain a reliable, interpretable, and non-parametric measure for correlation over multivariate sets. For the discovery of the top-k Correlated sets, we derive an effective algorithmic framework based on a tight bounding function. This framework offers exact, approximate, and heuristic search. Empirical evaluation shows that already for small sample sizes the estimator leads to low-regret optimization outcomes, while the algorithms are shown to be highly effective for both large and high-dimensional data. Through a case study we confirm that our discovery framework identifies interesting and meaningful correlations.
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discovering reliable correlations in categorical data
arXiv: Learning, 2019Co-Authors: Panagiotis Mandros, Mario Boley, Jilles VreekenAbstract:In many scientific tasks we are interested in discovering whether there exist any correlations in our data. This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of Attributes, how to do so without having to make assumptions on distribution of the data or the type of correlation, and, how to efficiently discover the top-most reliably Correlated Attribute sets from data. In this paper we answer these questions for discovery tasks in categorical data. In particular, we propose a corrected-for-chance, consistent, and efficient estimator for normalized total correlation, by which we obtain a reliable, naturally interpretable, non-parametric measure for correlation over multivariate sets. For the discovery of the top-k Correlated sets, we derive an effective algorithmic framework based on a tight bounding function. This framework offers exact, approximate, and heuristic search. Empirical evaluation shows that already for small sample sizes the estimator leads to low-regret optimization outcomes, while the algorithms are shown to be highly effective for both large and high-dimensional data. Through two case studies we confirm that our discovery framework identifies interesting and meaningful correlations.
Jiebo Luo - One of the best experts on this subject based on the ideXlab platform.
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Correlated Attribute transfer with multi task graph guided fusion
ACM Multimedia, 2012Co-Authors: Yahong Han, Qi Tian, Yueting Zhuang, Jiebo LuoAbstract:Due to the describable or human-nameable nature of visual Attributes, the Attribute-based methods have been receiving much attentions in recent years in many applications. The advantages of the utilization of visual Attributes are that they can be composed to create descriptions at various levels of specificity or they can be learned once and then applied to recognize new objects or categories. Therefore, Attribute prediction becomes an essential problem to boost image understanding. This paper proposes an approach for Correlated Attribute transfer from a well-defined source image set to an uncontrolled target image set for Attribute prediction. We call it Correlated Attribute transfer with multi-task graph-guided fusion (CAT-MtG2F). The novelty of CAT-MtG2F is to encourage highly Correlated Attributes to share a common set of relevant low-level features and transfer the learned common structure from the source image set to the target image set. The experiments show that the proposed CAT-MtG2F achieves better performance in Attribute prediction.
Panagiotis Mandros - One of the best experts on this subject based on the ideXlab platform.
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discovering reliable correlations in categorical data
International Conference on Data Mining, 2019Co-Authors: Panagiotis Mandros, Mario Boley, Jilles VreekenAbstract:In many scientific tasks we are interested in finding correlations in our data. This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of Attributes, how to do so without having to make assumptions on data distribution or the type of correlation, and, how to search efficiently for the most Correlated Attribute sets. We answer these questions for discovery tasks with categorical data. In particular, we propose a corrected-for-chance, consistent, and efficient estimator for normalized total correlation, in order to obtain a reliable, interpretable, and non-parametric measure for correlation over multivariate sets. For the discovery of the top-k Correlated sets, we derive an effective algorithmic framework based on a tight bounding function. This framework offers exact, approximate, and heuristic search. Empirical evaluation shows that already for small sample sizes the estimator leads to low-regret optimization outcomes, while the algorithms are shown to be highly effective for both large and high-dimensional data. Through a case study we confirm that our discovery framework identifies interesting and meaningful correlations.
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discovering reliable correlations in categorical data
arXiv: Learning, 2019Co-Authors: Panagiotis Mandros, Mario Boley, Jilles VreekenAbstract:In many scientific tasks we are interested in discovering whether there exist any correlations in our data. This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of Attributes, how to do so without having to make assumptions on distribution of the data or the type of correlation, and, how to efficiently discover the top-most reliably Correlated Attribute sets from data. In this paper we answer these questions for discovery tasks in categorical data. In particular, we propose a corrected-for-chance, consistent, and efficient estimator for normalized total correlation, by which we obtain a reliable, naturally interpretable, non-parametric measure for correlation over multivariate sets. For the discovery of the top-k Correlated sets, we derive an effective algorithmic framework based on a tight bounding function. This framework offers exact, approximate, and heuristic search. Empirical evaluation shows that already for small sample sizes the estimator leads to low-regret optimization outcomes, while the algorithms are shown to be highly effective for both large and high-dimensional data. Through two case studies we confirm that our discovery framework identifies interesting and meaningful correlations.
Yahong Han - One of the best experts on this subject based on the ideXlab platform.
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Correlated Attribute transfer with multi task graph guided fusion
ACM Multimedia, 2012Co-Authors: Yahong Han, Qi Tian, Yueting Zhuang, Jiebo LuoAbstract:Due to the describable or human-nameable nature of visual Attributes, the Attribute-based methods have been receiving much attentions in recent years in many applications. The advantages of the utilization of visual Attributes are that they can be composed to create descriptions at various levels of specificity or they can be learned once and then applied to recognize new objects or categories. Therefore, Attribute prediction becomes an essential problem to boost image understanding. This paper proposes an approach for Correlated Attribute transfer from a well-defined source image set to an uncontrolled target image set for Attribute prediction. We call it Correlated Attribute transfer with multi-task graph-guided fusion (CAT-MtG2F). The novelty of CAT-MtG2F is to encourage highly Correlated Attributes to share a common set of relevant low-level features and transfer the learned common structure from the source image set to the target image set. The experiments show that the proposed CAT-MtG2F achieves better performance in Attribute prediction.
Mario Boley - One of the best experts on this subject based on the ideXlab platform.
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discovering reliable correlations in categorical data
International Conference on Data Mining, 2019Co-Authors: Panagiotis Mandros, Mario Boley, Jilles VreekenAbstract:In many scientific tasks we are interested in finding correlations in our data. This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of Attributes, how to do so without having to make assumptions on data distribution or the type of correlation, and, how to search efficiently for the most Correlated Attribute sets. We answer these questions for discovery tasks with categorical data. In particular, we propose a corrected-for-chance, consistent, and efficient estimator for normalized total correlation, in order to obtain a reliable, interpretable, and non-parametric measure for correlation over multivariate sets. For the discovery of the top-k Correlated sets, we derive an effective algorithmic framework based on a tight bounding function. This framework offers exact, approximate, and heuristic search. Empirical evaluation shows that already for small sample sizes the estimator leads to low-regret optimization outcomes, while the algorithms are shown to be highly effective for both large and high-dimensional data. Through a case study we confirm that our discovery framework identifies interesting and meaningful correlations.
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discovering reliable correlations in categorical data
arXiv: Learning, 2019Co-Authors: Panagiotis Mandros, Mario Boley, Jilles VreekenAbstract:In many scientific tasks we are interested in discovering whether there exist any correlations in our data. This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of Attributes, how to do so without having to make assumptions on distribution of the data or the type of correlation, and, how to efficiently discover the top-most reliably Correlated Attribute sets from data. In this paper we answer these questions for discovery tasks in categorical data. In particular, we propose a corrected-for-chance, consistent, and efficient estimator for normalized total correlation, by which we obtain a reliable, naturally interpretable, non-parametric measure for correlation over multivariate sets. For the discovery of the top-k Correlated sets, we derive an effective algorithmic framework based on a tight bounding function. This framework offers exact, approximate, and heuristic search. Empirical evaluation shows that already for small sample sizes the estimator leads to low-regret optimization outcomes, while the algorithms are shown to be highly effective for both large and high-dimensional data. Through two case studies we confirm that our discovery framework identifies interesting and meaningful correlations.
