Multi-view unsupervised feature selection has been proven to be efficient in reducing the dimensionality of multi-view unlabeled data with high dimensions. The previous methods assume that all views are complete. However, real applications, often incomplete, i.e., some instances missing, which will result failure these methods. Besides, while arrive form streams, existing suffer issues storage ...

In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The will fill in the gap research relation between two types Based on Bayesian network framework information theory, first show that methods share same objective. That is find Markov blanket class attribute, theoretically optimal set for classification. We then examine assumptions made by when ...

Background: Couples seeking assisted reproductive donation procedures are faced with complex challenges throughout their treatment which can have important psychological impacts on their life. Selecting a suitable donor is one of the hardest decisions they will ever make. This study was carried out to provide an in-depth description of the experiences of couples in relation to donor selection. ...

We demonstrate F, a system for building regression models over database views. At its core lies the observation that the computation and representation of materialized views, and in particular of joins, entail non-trivial redundancy that is not necessary for the efficient computation of aggregates used for building regression models. F avoids this redundancy by factorizing data and computation ...