Dis) similarity measure is a significant component of vector model. In content based image retrieval the compatibility of (dis)similarity measure and representation technique is very important for effective and efficient image retrieval. In order to find a suitable dis-similarity measure for a particular representation technique experimental comparison is needed. This paper highlights some of t...

In this paper, we propose a non-parametric Bayesian framework for multi-modal hash learning that takes into account the distance supervision (similarity/dissimilarity constraints). Our model embeds data of arbitrary modalities into a single latent binary feature with the ability to learn the dimensionality of the binary feature using the data itself. Given supervisory information (labeled simil...

Overview Different vectorial and / or (dis)similarity representations can be produced for a given data. These distinct representations or data generating models have typically been used individually, in single classifiers or single clustering algorithms, or simultaneously, as in classifier combination techniques or cluster ensemble methods, depending, respectively, on whether working under a su...

Verifiable training has shown success in creating neural networks that are provably robust to a given amount of noise. However, despite only enforcing single robustness criterion, its performance scales poorly with dataset complexity. On CIFAR10, non-robust LeNet model 21.63% error rate, while created using verifiable and L-infinity criterion 8/255, an rate 57.10%. Upon examination, we find whe...

Common practice emphasizes significant sequence similarities between different members of protein families. These similarities presumably reflect on evolutionary conservation of structurally and functionally essential residues. The nonconserved regions, on the other hand, may be either selectively neutral or differentiated. We propose several distributional sequence statistics (e.g., clustering...

In this article, an extension of similarity-based approximation spaces by a relation of dissimilarity of objects is proposed. Apart from the usual approximation mappings, adapted to our case, mappings of exterior, possibly negative region, and ignorance region are introduced and investigated.