Online semi-supervised perception: Real-time learning without explicit feedback

TL;DR

This paper introduces a real-time semi-supervised learning algorithm that updates a graphical model online without explicit feedback, demonstrating superior face recognition performance on video datasets.

Contribution

It presents a novel online semi-supervised learning algorithm combining graph-based methods with real-time updates, validated on face recognition tasks.

Findings

Achieves real-time face recognition with high precision and recall

Provides a regret bound for the algorithm's solution quality

Effectively updates graphical representations with unlabeled data online

Abstract

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.