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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/135889


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/135889


    Title: Mimicking Complexity of Structured Data Matrix’s Information Content: Categorical Exploratory Data Analysis
    Authors: 周珮婷
    Chou, Elizabeth P.
    Hsieh, Fushing
    Chen, Ting-Li
    Contributors: 統計系
    Keywords: contingency-kD-lattice; high order structural dependency; heterogeneity; mutual conditional entropy matrix; principal component analysis (PCA)
    Date: 2021-05
    Issue Date: 2021-06-25 10:17:21 (UTC+8)
    Abstract: We develop Categorical Exploratory Data Analysis (CEDA) with mimicking to explore and exhibit the complexity of information content that is contained within any data matrix: categorical, discrete, or continuous. Such complexity is shown through visible and explainable serial multiscale structural dependency with heterogeneity. CEDA is developed upon all features’ categorical nature via histogram and it is guided by all features’ associative patterns (order-2 dependence) in a mutual conditional entropy matrix. Higher-order structural dependency of k(≥3) features is exhibited through block patterns within heatmaps that are constructed by permuting contingency-kD-lattices of counts. By growing k, the resultant heatmap series contains global and large scales of structural dependency that constitute the data matrix’s information content. When involving continuous features, the principal component analysis (PCA) extracts fine-scale information content from each block in the final heatmap. Our mimicking protocol coherently simulates this heatmap series by preserving global-to-fine scales structural dependency. Upon every step of mimicking process, each accepted simulated heatmap is subject to constraints with respect to all of the reliable observed categorical patterns. For reliability and robustness in sciences, CEDA with mimicking enhances data visualization by revealing deterministic and stochastic structures within each scale-specific structural dependency. For inferences in Machine Learning (ML) and Statistics, it clarifies, upon which scales, which covariate feature-groups have major-vs.-minor predictive powers on response features. For the social justice of Artificial Intelligence (AI) products, it checks whether a data matrix incompletely prescribes the targeted system.
    Relation: Entropy, Vol.23, No.5, pp.594
    Data Type: article
    DOI 連結: https://doi.org/10.3390/e23050594
    DOI: 10.3390/e23050594
    Appears in Collections:[統計學系] 期刊論文

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