Online semi-supervised perception: Real-time learning without explicit feedback
For real-time perception systems that cannot rely on explicit feedback, this work provides a practical algorithm with theoretical guarantees.
The paper proposes an online semi-supervised learning algorithm that combines graph-based semi-supervised learning with online learning for real-time perception tasks. Applied to face recognition, it achieves superior precision and recall on three challenging video datasets.
This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graphs and online learning. In particular, it iteratively builds a graphical representation of its world and updates it with observed examples. Labeled examples constitute the initial bias of the algorithm and are provided offline, and a stream of unlabeled examples is collected online to update this bias. We motivate the algorithm, discuss how to implement it efficiently, prove a regret bound on the quality of its solutions, and apply it to the problem of real-time face recognition. Our recognizer runs in real time, and achieves superior precision and recall on 3 challenging video datasets.