Deep embedded multi-view clustering with collaborative training

TL;DR: Guanyuezhen et al. as discussed by the authors proposed a unified one-step multi-view spectral clustering method, which integrates the spectral embedding and k-means into a unified framework to obtain discrete clustering labels with a one step strategy.

TL;DR: Multi-view clustering (MVC), which groups data samples by leveraging complementary and consensual information from several views, is gaining popularity as discussed by the authors . But despite the rapid evolution of MVC approaches, there has yet to be a study that provides a full MVC roadmap for both stimulating technical improvements and orienting research newbies to MVC.

TL;DR: The proposed multiplet loss explores the complementary information between the structural information and neighbor information to enlarge the inter-class variation, as well as adds an upper bound loss to achieve the finite distance between positive embeddings and anchorembeddings for reducing the intra- class variation.

TL;DR: SDMVC as mentioned in this paper concatenates all views' embedded features to form the global features, which can overcome the negative impact of some views' unclear clustering structures, and then, pseudo-labels are obtained to build a unified target distribution to perform multi-view discriminative feature learning.

TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.

TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.

TL;DR: A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well.