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首页> 外文会议>Independent Component Analyses, Compressive Sampling, Large Data Analyses, Neural Networks, Biosystems, and Nanoengineering XIII >E-IQ IQ KNOWLEDGE MINING FOR GENERALIZED LDA
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E-IQ IQ KNOWLEDGE MINING FOR GENERALIZED LDA

机译：通用LDA的E-I和I知识挖掘

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摘要

How can the human brain uncover patterns, associations and features in real-time, real-world data? There must be a general strategy used to transform raw signals into useful features, but representing this generalization in the context of our information extraction tool set is lacking. In contrast to Big Data (BD), Large Data Analysis (LDA) has become a reachable multi-disciplinary goal in recent years due in part to high performance computers and algorithm development, as well as the availability of large data sets. However, the experience of Machine Learning (ML) and information communities has not been generalized into an intuitive framework that is useful to researchers across disciplines. The data exploration phase of data mining is a prime example of this unspoken, ad-hoc nature of ML - the Computer Scientist works with a Subject Matter Expert (SME) to understand the data, and then build tools (i.e. classifiers, etc.) which can benefit the SME and the rest of the researchers in that field. We ask, why is there not a tool to represent information in a meaningful way to the researcher asking the question? Meaning is subjective and contextual across disciplines, so to ensure robustness, we draw examples from several disciplines and propose a generalized LDA framework for independent data understanding of heterogeneous sources which contribute to Knowledge Discovery in Databases (KDD). Then, we explore the concept of adaptive Information resolution through a 6W unsupervised learning methodology feedback system. In this paper, we will describe the general process of man-machine interaction in terms of an asymmetric directed graph theory (digging for embedded knowledge), and model the inverse machine-man feedback (digging for tacit knowledge) as an ANN unsupervised learning methodology. Finally, we propose a collective learning framework which utilizes a 6W semantic topology to organize heterogeneous knowledge and diffuse information to entities within a society in a personalized way.
机译：人脑如何发现实时，真实世界数据中的模式，关联和特征？必须有一种将原始信号转换为有用功能的通用策略，但是在我们的信息提取工具集的背景下却缺乏这种概括。与大数据（BD）相比，大数据分析（LDA）近年来已成为可实现的多学科目标，部分原因是高性能的计算机和算法开发以及大数据集的可用性。但是，机器学习（ML）和信息社区的经验尚未被概括为一个直观的框架，该框架对跨学科的研究人员有用。数据挖掘的数据探索阶段就是ML的这种不加言说的即席性质的主要示例-计算机科学家与主题专家（SME）合作以理解数据，然后构建工具（即分类器等）。这可以使中小型企业和该领域的其他研究人员受益。我们问，为什么没有一种工具可以对研究者提出有意义的信息呢？含义是跨学科的主观和上下文，因此，为了确保稳健性，我们从多个学科中借鉴示例，并提出了一个通用的LDA框架，用于对异构源的独立数据进行理解，这有助于数据库中的知识发现（KDD）。然后，我们通过6W无监督学习方法反馈系统探索自适应信息分辨率的概念。在本文中，我们将根据非对称有向图理论（挖掘嵌入式知识）描述人机交互的一般过程，并将逆机器人反馈（挖掘隐性知识）建模为ANN无监督学习方法。最后，我们提出了一个集体学习框架，该框架利用6W语义拓扑来组织异构知识并以个性化方式将信息传播到社会中的实体。

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