Statistical machine learning for data mining and collaborative multimedia retrieval

of thesis entitled: Statistical Machine Learning for Data Mining and Collaborative Multimedia Retrieval Submitted by HOI, Chu Hong (Steven) for the degree of Doctor of Philosophy at The Chinese University of Hong Kong in September 2006 Statistical machine learning techniques have been widely applied in data mining and multimedia information retrieval. While traditional methods, such as supervised learning, unsupervised learning, and active learning, have been extensively studied separately, there are few comprehensive schemes to investigate these techniques in a unified approach. This thesis proposes a unified learning paradigm (ULP) framework that integrates several machine learning techniques including supervised learning, unsupervised learning, semi-supervised learning, active learning and metric learning in a synergistic way to maximize the effectiveness of a learning task. Based on this unified learning framework, a novel scheme is suggested for learning Unified Kernel Machines (UKM). The UKM scheme combines supervised kernel machine learning, unsupervised kernel design, semi-supervised kernel learning, and active learning in an effective fashion. A key component in the UKM scheme is to learn kernels from both labeled and unlabeled data. To this purpose, a new Spectral Kernel Learning (SKL) algorithm is proposed, which is related to a quadratic program. Empirical results show that the UKM technique is promising for classification tasks.