Deep subspace clustering networks have attracted much attention in clustering, which an auto-encoder non-linearly maps the input data into a latent space, and fully connected layer named self-expressiveness module is introduced to learn affinity matrix via typical regularization term (e.g., sparse or low-rank). However, adopted terms ignore connectivity within each subspace, limiting their perf...

In classification problems, lack of knowledge of the prior distribution may make the application of Bayes’ rule inadequate. Uniform or arbitrary priors may often provide classification answers that, even in simple examples, may end up contradicting our common sense about the problem. Entropic priors, via application of the maximum entropy principle, seem to provide a much better answer and can ...

In the framework of multicriteria decision making based on Choquet integrals, we present a maximum entropy like method enabling to determine, if it exists, the least specific capacity compatible with the initial preferences of the decision maker.