Broad Learning via Fusion of Heterogeneous Information

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1 Broad Learning via Fusion of Heterogeneous Information Philip S. Yu Distinguished Professor & Wexler Chair University of Illinois at Chicago Dean, Institute for Data Science Tsinghua University

2 Big Data Data Stream with Concept Drift High Dimensional Data Heterogeneous Data Sources Unconventional Data Types Graph/Network Sequence Text Variety Velocity BIG Data R Value Veracity Cleanness Trustworthiness Privacy Volume Scalable Mining Algorithms

3 Deep and Broad Learning In addition to depth, we also need breadth through fusion of multiple (heterogeneous) data sources Fusion of Heterogeneous Data Sources DS n Graph DS 2 Image DS 1 Text Deep Learning on Each Source

4 Myth on Big Data Not every dataset is large or complete Many other datasets may be leveraged

5 Broad Learning In the era of big data, all kinds of data are available. To achieve better learning/mining performance on solving real world problem, we need to be able to 1. Identify and acquire the relevant and useful data sources 2. Devise a model to fuse the information from heterogeneous data sources 3. Mine information from each data source deeply based on the need of the overall model

6 Types of Broad Learning Different types of information on the same entity Multi-view Learning Information on different, but similar type of entities Transfer Learning Information on different types of entities that can be related via a complex network-type of relations HIN based fusion

7 Real World Examples Traffic prediction Air pollution prediction Stock price prediction Disease diagnostic Drug discovery Product recommendation Friend recommendation POI recommendation Mobile user identification/authentication

8 Mobile User Identification Mobile phones, in particular smart phones, have become near ubiquitous with 2 billion smart phone users worldwide Mobile phones are increasingly being used for diverse applications, including payment Mobile device security and user identification is becoming critical. User Behavior Biometric: a promising direction

9 Using Behavioral Biometrics for Mobile User Identification What kind of biometrics will be sufficient? How does one capture user characteristics from the biometrics? What accuracy of the mobile user identification can be achieved?

10 Using Behavioral Biometrics for Mobile User Identification Our goal is to identify the user of a mobile device using easily collectable time series data from multiple sensors on a mobile device. Accelerometer Keyboard Mood detection Gyroscope GPS

11 Multi-view Data Time series data on alphanumeric characters Only capturing the time and movement distance, instead of the character itself for privacy consideration. Time series data on special characters auto-correct, backspace, space, etc Time series data on accelerometer values

12 Challenges Unaligned views Different sampling time at each view Dominant views Accelerometer values recorded every 60ms View Interactions

13 View of Alphabet Typing Behavior Top 5 active users

14 View of Symbol/Number Typing Behavior Top 5 active users

15 View of Acceleration Behavior Top 5 active users

16 Concatenation Given multiple time series X, Y and Z Case I: Concatenation at time point level resulting in multiple attribute time series (X t1, Y t1, Z t1 ), (X t2, Y t2, Z t3 ),.., (X tn, Y tn, Z tn ) Case 2: Concatenation at time series level resulting in a long feature vector (X 1, X 2, X n, Y 1, Y 2,., Y m, Z 1, Z 2,., Z h )

17 Late Fusion Approach

18 Identification between Any Pair of Users F1 Average: 98.97% Accuracy Average: 99.1% TOP Ten Users

19 N-active Shared User Identification

20 Summary Examine concept of broad learning In addition to depth, we also need breadth through fusion of multiple (heterogeneous) data sources Apply behavioral biometrics for mobile user identification Develop multiple view broad learning on behavioral biometrics Preliminary results look promising