1 Learning Techniques for Big Data Redundant features – Group lasso – Feature selection

2 Learning Techniques for Big Data Online Learning for Group Lasso Scenario: huge data with group features appear sequentially How to learn the decision function adaptively?

3 Learning Techniques for Big Data Solutions and Properties Objective function Algorithm Three main steps Close solution Theoretical guarantee

4 Learning Techniques for Big Data Redundant features – Group lasso – Feature selection Insufficient labeled data – Multi-task learning – Unsupervised learning

5 Learning Techniques for Big Data Online Learning for Multi-task Feature Selection Problems and Motivation – Learning multiple related tasks simultaneously to improve performance – Existing redundant or irrelevant features – Data occur sequentially Challenges – How to adaptively update the models while selecting the important features? – Any theoretical guarantee? aMTFS

6 Learning Techniques for Big Data Solution and Properties Objective function Algorithm Three main steps Close-formed solution Theoretical guarantee

7 Learning Techniques for Big Data Redundant features – Group lasso – Feature selection Insufficient labeled data – Multi-task learning – Unsupervised learning Complicated decision function – Multiple kernel learning: level method speedup, generalization

8 Learning Techniques for Big Data Sparse Generalized Multiple Kernel Learning Data characteristics – Multi-source – Heterogeneous Labeled data: Horse/Donkey

9 Learning Techniques for Big Data Redundant features – Group lasso – Feature selection Insufficient labeled data – Multi-task learning – Unsupervised learning Complicated decision function – Multiple kernel learning: level method speedup, generalization Multiple kernel learning Volume data – Online learning Related work – ICML’10, CIKM’10-11, IJCNN’10, IEEETNN’11, ACMTKDD’13