We present a probabilistic framework for learning pairwise similarities between objects belonging to different modalities, such as drugs and proteins, or text and images. Our framework is based on learning a binary code based representation for objects in each modality, and has the following key properties: (i) it can leverage both pairwise as well as easy-to-obtain relative preference based cr...

We consider lower bounding the maximum achievable rate of a differential unitary spacetime transmission subject to a prescribed performance. We characterize the performance using a suitably defined distance between two unitary matrices, which was previously obtained through pairwise error probability analysis. Considering the set of all unitary or orthogonal matrices as a compact manifold with ...

We present a family of adaptive pairwise tournaments which are provably robust against large error fractions when used to determine the largest element in a set. These tournaments use nk pairwise comparisons but have only O(k + log n) depth where n is the number of players and k is a robustness parameter (for reasonable values of n and k). We show how these tournaments can be used to prove mult...

For existing kernel learning based semi-supervised clustering algorithms, it is generally difficult to scale well with large scale datasets and robust pairwise constraints. In this paper, we proposed a new Non-Parametric Kernel Learning framework (NPKL) to deal with these problems. We generalized the graph embedding framework into kernel learning, by reforming it as a semi-definitive programmin...

To deal with the problem of insufficient labeled data, usually side information – given in the form of pairwise equivalence constraints between points – is used to discover groups within data. However, existing methods using side information typically fail in cases with high-dimensional spaces. In this paper, we address the problem of learning from side information for high-dimensional data. To...

In this paper, we propose a method of clustering large image sets using human input. We assume an algorithm provides us with pairwise similarities. We then actively ask for more accurate pairwise similarities between images from humans. Using all similarities, we cluster the images and show that the improvement gain is significant even when the available human resources are very limited.

A critical problem related to kernel-based methods is the selection of an optimal kernel for the problem at hand. The kernel function in use must conform with the learning target in order to obtain meaningful results. While solutions to estimate optimal kernel functions and their parameters have been proposed in a supervised setting, the problem presents open challenges when no labeled data are...

Although support vector machine (SVM) has become a powerful tool for pattern classification and regression, a major disadvantage is it fails to exploit the underlying correlation between any pair of data points as much as possible. Inspired by the modified pairwise constraints trick, in this paper, we propose a novel classifier termed as support vector machine with hypergraph-based pairwise con...

Semi-supervised clustering of images has been an interesting problem for machine learning and computer vision researchers for decades. Pairwise constrained clustering is a popular paradigm for semi supervision that uses knowledge about whether two images belong to the same category (must-link constraint) or not (can’t-link constraint). Performance of constrained clustering algorithms can be imp...