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Segal et al. [10] proposed a framework for the unsupervised knowledge-driven analysis of expression data.
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At the micro-level, this experimental framework provides a new approach for the unsupervised collection of social interaction data, opening the path to the understanding and characterization of interaction mechanisms that represent the basic ingredients of realistic agent-based models for diseases and information spreading phenomena.
To have an objective and automated framework, we used the unsupervised approach, using the "mclust" package [ 51] of R statistical language.
In this paper, we propose a pure image-driven unsupervised boundary delineation framework for the automated neural foramina boundary delineation.
Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs.
A framework of using t mixture models with fourteen eigen-decomposed covariance structures for the unsupervised learning of heterogeneous multivariate data with possible missing values is designed and implemented.
In this paper, we propose a new feature-level self-representation framework for unsupervised feature selection.
We have presented an open source framework for unsupervised protein-ligand binding affinity (free energy) computation using iterative linear interaction energy (LIE) theory.
We then develop a data-driven framework for a fully unsupervised extraction of such representations.
Secondly, the methods they selected included supervised and unsupervised approaches which, in our view, could be unfair for the unsupervised ones.
Autoencoder is a very powerful method for the unsupervised learning of high-level data representations.
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