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　　摘要

　　转自：郑宇MSRA

　　跨域数据融合全套PPT分章节全部公开（300MB+），算法结合案例，助力攻克数据挖掘和机器学习新难点，抢占大数据和人工智能制高点。

　　1. Overview

　　Traditional data mining usually deals with data from a single domain. In the big data era, we face a diversity of datasets from different sources in different domains. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density. How to unlock the power of knowledge from multiple disparate (but potentially connected) datasets is paramount in big data research, essentially distinguishing big data from traditional data mining tasks. This calls for advanced techniques that can fuse the knowledge from various datasets organically in a machine learning and data mining task. These methods focus on knowledge fusion rather than schema mapping and data merging, significantly distinguishing between cross-domain data fusion and traditional data fusion studied in the database community.

　　Figure 1. The difference between cross-domain data fusion and conventional data fusion

　　This tutorial summarizes the data fusion methodologies, classifying them into three categories: stage-based, feature level-based, and semantic meaning-based data fusion methods. The last category of data fusion methods is further divided into four groups: multi-view learning-based, similarity-based, probabilistic dependency-based, and transfer learning-based methods.

　　Figure 2 Categories of methods for cross-domain data fusion

　　This tutorial does not only introduce high-level principles of each category of methods, but also give examples in which these techniques are used to handle real big data problems. In addition, this tutorial positions existing works in a framework, exploring the relationship and difference between different data fusion methods. This tutorial will help a wide range of communities find a solution for data fusion in big data projects.

　　2. The Stage-Based Data Fusion Methods

　　This category of methods uses different datasets at the different stages of a data mining task. So, different datasets are loosely coupled, without any requirements on the consistency of their modalities. the stage-based data fusion methods can be a meta-approach used together with other data fusion methods. For example, Yuan et al. [3] first use road network data and taxi trajectories to build a region graph, and then propose a graphical model to fuse the information of POIs and the knowledge of the region graph. In the second stage, a probabilistic-graphical-model-based method is employed in the framework of the stage-based method.

　　Figure 3. Illustration of the stage-based data fusion

　　Examples:

　　As illustrated in Fig. 3 A), Zheng et al. first partition a city into regions by major roads using a map segmen-tation method. The GPS trajectories of taxicabs are then mapped onto the regions to formulate a region graph, as depicted in Fig. 3 B), where a node is a region and an edge denotes the aggregation of commutes (by taxis in this case) between two regions. The region graph actually blends knowledge from the road net-work and taxi trajectories. By analyzing the region graph, a body of research has been carried out to identi-fy the improper design of a road network, detect and diagnose traffic anomalies as well as find urban functional regions.

　　Figure 4. An example of using the stage-based method for data fusion

　　链接：

　　https://www.microsoft.com/en-us/research/project/cross-domain-data-fusion/

　　原文链接：

　　https://weibo.com/2073091511/EDGDDyWNU?ref=home&rid=7_0_202_2778227504193407143&type=comment

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