In this paper we introduce a novel hash learning framework that has two main distinguishing features, when compared to past approaches. First, it utilizes codewords in the Hamming space as ancillary means to accomplish its hash learning task. These codewords, which are inferred from the data, attempt to capture similarity aspects of the data’s hash codes. Secondly and more importantly, the same...

We apply the framework of kernel dimension reduction, originally designed for supervised problems, to unsupervised dimensionality reduction. In this framework, kernel-based measures of independence are used to derive low-dimensional representations that maximally capture information in covariates in order to predict responses. We extend this idea and develop similarly motivated measures for uns...

Correlation between instances is often modelled via a kernel function using input attributes of the instances. Relational knowledge can further reveal additional pairwise correlations between variables of interest. In this paper, we develop a class of models which incorporates both reciprocal relational information and input attributes using Gaussian process techniques. This approach provides a...

Semi-supervised learning has emerged as a popular framework for improving modeling accuracy while controlling labeling cost. Based on an extension of stochastic composite likelihood we quantify the asymptotic accuracy of generative semi-supervised learning. In doing so, we complement distributionfree analysis by providing an alternative framework to measure the value associated with different l...

This paper presents a theoretical analysis of multi-view embedding – feature embedding that can be learned from unlabeled data through the task of predicting one view from another. We prove its usefulness in supervised learning under certain conditions. The result explains the effectiveness of some existing methods such as word embedding. Based on this theory, we propose a new semi-supervised l...

This paper presents a weak supervised evaluation framework for definition question answering (DefQA) called Solon. It automatically evaluates a set of DefQA systems using existing human definitions as gold standard models. This allows the framework to overcome known limitations of the evaluation methods in the state of the art with the advantage that it is less supervised. In addition, Solon ad...

This paper introduces a supervised discriminant Hausdorff distance that fits into the framework for automatic face analysis and recognition proposed in [1]. Our proposal relies solely on face shape variation contrarily to most of the successful model-based approaches, and results show comparable performance to them. The whole framework is based in a new set of Hausdorff measures and defines fac...

Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised hashing. Recently, discrete supervised hashing and deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a discrete optimization problem. Hence, util...

We introduce a boosting framework to solve a classification problem with added manifold and ambient regularization costs. It allows for a natural extension of boosting into both semisupervised problems and unsupervised problems. The augmented cost is minimized in a greedy, stagewise functional minimization procedure as in GradientBoost. Our method provides insights into generalization issues in...