Clustering performance can often be greatly improved by
leveraging side information. In this paper, we consider constrained
clustering with pairwise constraints, which specify
s...
Statistical methods, such as independent component analysis, have been successful in learning local low-level features from natural image data. Here we extend these methods for le...
In this paper, we address the pre-image problem in kernel principal component analysis (KPCA). The preimage problem finds a pattern as the pre-image of a feature vector defined in...
We introduce a method that enables scalable image search for learned metrics. Given pairwise similarity and dissimilarity constraints between some images, we learn a Mahalanobis d...
In this paper, we propose a novel component-wise smoothing algorithm that constructs a hierarchy (or family) of smoothened log-likelihood surfaces. Our approach first smoothens th...