Topic
Pairwise comparison
About: Pairwise comparison is a research topic. Over the lifetime, 6804 publications have been published within this topic receiving 174081 citations.
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TL;DR: A new scoring function in MMM is set up to deliver more accurate target-template alignments by developing and incorporating into the composite scoring function a novel statistical pairwise potential that combines local and non-local terms.
Abstract: Improvements in comparative protein structure modeling for the remote target-template sequence similarity cases are possible through the optimal combination of multiple template structures and by improving the quality of target-template alignment. Recently developed MMM and M4T methods were designed to address these problems. Here we describe new developments in both the alignment generation and the template selection parts of the modeling algorithms. We set up a new scoring function in MMM to deliver more accurate target-template alignments. This was achieved by developing and incorporating into the composite scoring function a novel statistical pairwise potential that combines local and non-local terms. The non-local term of the statistical potential utilizes a shuffled reference state definition that helped to eliminate most of the false positive signal from the background distribution of pairwise contacts. The accuracy of the scoring function was further increased by using BLOSUM mutation table scores.
44 citations
TL;DR: This work proposes Multi^2Test, a generalization of the previous work, for ordering multiple learning algorithms on multiple data sets from ''best'' to ''worst'' where the authors' goodness measure is composed of a prior cost term additional to generalization error.
44 citations
TL;DR: In this article, a Siamese network is used to encourage pairs of data points to output similar representations in the latent space, and a pair-based model allows augmenting the information with labeled pairs to constitute a semi-supervised framework.
Abstract: Recently, there has been increasing interest to leverage the competence of neural networks to analyze data. In particular, new clustering methods that employ deep embeddings have been presented. In this paper, we depart from centroid-based models and suggest a new framework, called Clustering-driven deep embedding with PAirwise Constraints (CPAC), for nonparametric clustering using a neural network. We present a clustering-driven embedding based on a Siamese network that encourages pairs of data points to output similar representations in the latent space. Our pair-based model allows augmenting the information with labeled pairs to constitute a semi-supervised framework. Our approach is based on analyzing the losses associated with each pair to refine the set of constraints. We show that clustering performance increases when using this scheme, even with a limited amount of user queries. We demonstrate how our architecture is adapted for various types of data and present the first deep framework to cluster three-dimensional (3-D) shapes.
44 citations
TL;DR: In this paper, the AHP and one of its variants have the potential to reach the wrong conclusion under certain circumstances, under the assumption that pairwise comparisons, which are used in these methods, take on continuous values.
44 citations
TL;DR: A hybrid model that uses concepts from fuzzy logic and analytical hierarchy process (AHP) is proposed and shows that this method provides intuitively promising results and that can be used for explaining route choice process of drivers.
44 citations