Robust unsupervised subspace learning for visual representation

• Online Access: http://hdl.handle.net/2047/D20260457

As the digital technology develops, a large amount of visual data (images and videos) are captured and shared every day in the social media. It is the fact that most data suffer from lack of tags or labels for various reasons, e.g. high labeling cost, etc. To represent the unlabeled visual data, extensive efforts have been made in both industry and academia. For instance, Apple launched their face clustering application in iPhone iOS-10 system to help users organize their photos by identity. However, it suffers from many criticisms, including poor clustering performance with occluded faces, non-frontal faces etc. The fundamental problem behind this challenging problem is, how to robustly represent the photos under different variations, such as head poses, lighting conditions, occlusions, or even larger corruptions. In this dissertation, we focus on solving robust visual data representation problems using unsupervised subspace learning (clustering) techniques.