Large-Scale Multi-view Data Analysis

Key Info
Description - a brief synopsis, abstract or summary of what the learning resource is about: 
Multi-view data are extensively accessible nowadays, since various types of features, viewpoints, and different sensors. For example, the most popular commercial depth sensor Kinect uses both visible light and near-infrared sensors for depth estimation; automatic driving uses both visual and radar sensors to produce real-time 3D information on the road, and face analysis algorithms prefer face images from different views for high-fidelity reconstruction and recognition. All of them tend to facilitate better data representation in different application scenarios. Essentially, multiple features attempt to uncover various knowledge within each view to alleviate the final tasks, since each view would preserve both shared and private information. This becomes increasingly common in the era of “Big Data” where the data are on large-scale, subject to corruption, generated from multiple sources, and have complex structures. While these problems attracted substantial research attention recently, a systematic overview of multi-view learning for Big Data analysis has never been given. In the face of big data and challenging real-world applications, we summarize and go through the most recent multi-view learning techniques appropriate to different data-driven problems. Specifically, our tutorial covers most multi-view data representation approaches, centered around two major applications along with Big Data, i.e., multi-view clustering, multi-view classification. In addition, it discusses current and upcoming challenges. This would benefit the community in both industry and academia from literature review to future directions.
This tutorial, available in PDF format,  is one of nine tutorials from the 2018 IEEE international conference on BIG DATA in Seattle WA, also you reach out to the others at IEEE Big Data 2018 Tutorials.
Authoring Person(s) Name: 
Yun Fu
Ming Shao
Zhengming Ding
Authoring Organization(s) Name: 
Northeastern University
Access Cost: 
No fee
Primary language(s) in which the learning resource was originally published or made available: 
More info about
Keywords - short phrases describing what the learning resource is about: 
Big data
Data analysis
Data integration
Data management
Data modeling
Data visualization
Published / Broadcast: 
Thursday, December 13, 2018
Publisher - organization credited with publishing or broadcasting the learning resource: 
2018 IEEE international conference on BIG DATA, Seattle WA
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Presentation - representation of the particular way in which an author shows, describes or explains one or more concepts, e.g., a set of Powerpoint slides.
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.
Target Audience - intended audience for which the learning resource was created: 
Early-career research scientist
Graduate student
Mid-career research scientist
Research faculty
Research scientist
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
Up to 1 hour