Multiple kernel clustering based on centered kernel alignment

TL;DR: A large number of multi-view clustering algorithms are summarized, a taxonomy according to the mechanisms and principles involved is provided, and a few examples for how these techniques are used are given.

TL;DR: A novel multi-view clustering method that works in the GBS framework is also proposed, which can construct data graph matrices effectively, weight each graph matrix automatically, and produce clustering results directly.

TL;DR: Experimental results show that the proposed method has outstanding performance among other excellent approaches on identifying drug-side effect associations, and compared with many existing methods, the proposed approach achieves better results on three benchmark datasets of drug side-effect associations.

TL;DR: This paper proposes a multi-kernel SVM to predict subcellular localization of both multi-location and single-location proteins, and makes use of the evolutionary information extracted from position specific scoring matrix (PSSM) and physicochemical properties of proteins, by Chou's general PseAAC and other efficient functions.

TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.

TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.

TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.

TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.