From patterned response dependency to structured covariate dependency: Entropy based categorical-pattern-matching.
From patterned response dependency to structured covariate dependency: Entropy based categorical-pattern-matching.
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DOI：
10.1371/journal.pone.0198253
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
McCowan B
中科院分区：
文献类型：
--
作者：
Fushing H;Liu SY;Hsieh YC;McCowan B
从感兴趣的系统生成的数据通常由许多协变量特征的测量值组成，并且可能包括指定集合中所有受试者的多个响应特征。这样的数据自然地由一个响应矩阵对一个协变量矩阵表示。矩阵格是一个有利的平台，可以同时容纳异构数据类型：连续，离散和分类，并探索特征和主题之间隐藏的依赖关系。在每个特征相对于其自身的直方图被单独重新归一化之后，根据组合信息理论针对所有对响应和协变量特征评估互条件熵的分类版本。然后，通过对这样的互条件熵矩阵应用数据几何（DCG）算法计算，划分多个协同特征组。独特的协同特征--群体包含独特的依赖结构。协同功能的成员之间的依赖关系的显式细节被看作是通过多尺度组合物的块计算的计算范式称为数据力学。然后，我们提出了一个分类模式匹配的方法来建立一个有向的关联链接：从模式化的响应依赖序列结构的协变量依赖。这种有向关联链接的图形显示被称为信息流，关联度通过树到树的互条件熵进行评估。通过五个数据集说明了这种新的发现系统知识的通用方法。在每一种情况下，涌现的可见异质性都是一种已发现知识的组织。
Data generated from a system of interest typically consists of measurements on many covariate features and possibly multiple response features across all subjects in a designated ensemble. Such data is naturally represented by one response-matrix against one covariate-matrix. A matrix lattice is an advantageous platform for simultaneously accommodating heterogeneous data types: continuous, discrete and categorical, and exploring hidden dependency among/between features and subjects. After each feature being individually renormalized with respect to its own histogram, the categorical version of mutual conditional entropy is evaluated for all pairs of response and covariate features according to the combinatorial information theory. Then, by applying Data Could Geometry (DCG) algorithmic computations on such a mutual conditional entropy matrix, multiple synergistic feature-groups are partitioned. Distinct synergistic feature-groups embrace distinct structures of dependency. The explicit details of dependency among members of synergistic features are seen through mutliscale compositions of blocks computed by a computing paradigm called Data Mechanics. We then propose a categorical pattern matching approach to establish a directed associative linkage: from the patterned response dependency to serial structured covariate dependency. The graphic display of such a directed associative linkage is termed an information flow and the degrees of association are evaluated via tree-to-tree mutual conditional entropy. This new universal way of discovering system knowledge is illustrated through five data sets. In each case, the emergent visible heterogeneity is an organization of discovered knowledge.
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影响因子：
--
作者：
SHANNON, CE
通讯作者：
SHANNON, CE
影响因子：
2.6
作者：
LAFI, SQ;KANEENE, JB
通讯作者：
KANEENE, JB
影响因子：
56.9
作者：
ANDERSON, PW
通讯作者：
ANDERSON, PW
影响因子：
3.7
作者：
Fushing, Hsieh;Chen, Chen
通讯作者：
Chen, Chen
DOI：
10.2307/2980781
发表时间：
1951-01-01
影响因子：
2
作者：
HOUTHAKKER, HS
通讯作者：
HOUTHAKKER, HS