We study zero-shot learning (ZSL) as a transfer learning problem, and focus on the two key aspects of ZSL, model effectiveness and model adaptation. For effective modeling, we adopt the boosting strategy to learn a zero-shot classifier from weak models to a strong model. For adaptable knowledge transfer, we devise a Semantic Correlation Regularization (SCR) approach to regularize the boosted model to be consistent with the inter-class semantic correlations. With SCR embedded in the boosting objective, and with a self-controlled sample selection for learning robustness, we propose a unified framework, Boosted Zero-shot classification with Semantic Correlation Regularization (BZ-SCR). By balancing the SCR-regularized boosted model selection and the self-controlled sample selection, BZ-SCR is capable of capturing both discriminative and adaptable feature-to-class semantic alignments, while ensuring the reliability and adaptability of the learned samples. The experiments on two ZSL datasets show the superiority of BZ-SCR over the state-of-the-arts.

We study content-based learning to rank from the perspective of learning distance functions. Standardly, the two key issues of learning to rank, feature mappings and score functions, are usually modeled separately, and the learning is usually restricted to modeling a linear distance function such as the Mahalanobis distance. However, the modeling of feature mappings and score functions are mutually interacted, and the patterns underlying the data are probably complicated and nonlinear. Thus, as a general nonlinear distance family, the Bregman distance is a suitable distance function for learning to rank, due to its strong generalization ability for distance functions, and its nonlinearity for exploring the general patterns of data distributions. In this paper, we study learning to rank as a structural learning problem, and devise a Bregman distance function to build the ranking model based on structural SVM. To improve the model robustness to outliers, we develop a robust structural learning framework for the ranking model. The proposed model Robust Structural Bregman distance functions Learning to Rank (RSBLR) is a general and unified framework for learning distance functions to rank. The experiments of data ranking on real-world datasets show the superiority of this method to the state-of-the-art literature, as well as its robustness to the noisily labeled outliers.

Learning to rank is an important task for many data mining applications. Essentially, the goal of learning to rank is to learn an appropriate similarity or distance metric to determine the relevance relationships among data points. However, most of the existing approaches for distance metric learning are limited in three aspects. First, they often assume a fixed form of distance metric for the entire input space. Second, the assumed distance functions are often computationally expensive or even intractable to learn for high dimensional data, such as Mahalanobis distance. Third, most of these approaches lack robustness to noisily labeled data, which is pervasive in many real-world applications. In this paper, we study learning to rank as a problem of distance metric learning to address the above three problems. We choose Bregman distance as the target distance function, due to its general functional form as a generalization of a wide class of distance functions, and its capacity of exploiting complicated nonlinear patterns underlying the data. Under the framework of structural SVM, we formulate the problem of learning Bregman distance functions for ranking as a QP problem by a nonparametric approach, and present an effective algorithm. Furthermore, we propose a self-reinforcement scheme that adaptively differentiates each data point in the role of learning to secure the robustness. We emphasize that the proposed method SBLR-S (Structural Bregman distance functions Learning to Rank with Self-reinforcement) is more general than the conventional distance metric learning approaches, and is able to handle high dimensional data as well as noisily labeled data. The experiments of data ranking on real-world datasets show the superiority of this method to the state-of-the-art literature.