Activity Recognition via Social Knowledge Transfer

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Activity Recognition via Social Knowledge Transfer"

By

Mr. Yin ZHU


Abstract

In today's world, we have increasingly sophisticated means of recording the 
daily activity of humans as well as other moving objects in both physical and 
virtual worlds. These recorded activities include phone calls, uses of Apps on 
smartphones, and expression of opinions over social-network objects such as 
photos. These activities and actions give rise to a huge amount of data. 
Activity recognition aims to understand users' actions and intentions based on 
models built from these data. Accurate activity recognition allows us to track 
people's daily activities, to recognize the semantic functions of places, to 
help users and managers identify spammers in an online social network, and to 
predict the future activity levels of social-network users.

In this thesis, we study a special kind of activity recognition problems in 
which a social network structure is available or abundant social activity 
records are given as external knowledge sources. We call these two kinds of 
auxiliary knowledge sources as social knowledge. Utilizing them has two unique 
challenges. First, how to utilize the rich knowledge in the social structure 
and model it for the activity recognition model for each user in the social 
network. Second, while the data is abundant in general, sometimes the labeled 
data is limited due to reasons such as short usage time and inactiveness of 
some users and high cost to label data in both physical and virtual worlds. 
Transfer learning is a new learning framework that is suitable to model 
external knowledge sources and becomes especially effective when the target 
problem domain suffers from data sparsity issues. In the past decade, transfer 
learning has been successfully applied to application domains such as text 
mining, image understanding, and recommender systems. We propose a novel 
transfer learning framework that uses auxiliary social knowledge to improve 
activity recognition tasks, and apply it to four specific problems: social 
spammer detection, semantic place prediction, social activity level prediction, 
and heterogeneous transfer from online social activities to the physical world. 
Our experimental results on the four specific recognition tasks all demonstrate 
the high effectiveness of the proposed transfer learning framework.


Date:			Monday, 21 July 2014

Time:			9:30am - 11:30am

Venue:			Room 3501
 			Lifts 25/26

Chairman:		Prof. Bertram Shi (ECE)

Committee Members:	Prof. Qiang Yang (Supervisor)
 			Prof. Raymond Wong
 			Prof. Dit-Yan Yeung
 			Prof. Rong Zheng (ISOM)
                        Prof. Irwin King (Comp. Sci., & Engg., CUHK)


**** ALL are Welcome ****