Using a Markov chain perspective of spectral clustering we present an algorithm to automatically find the number of stable clusters in a dataset. The Markov chain's behaviour is characterized by the spectral pr
This work bridges the gap between two popular methodologies for data partitioning:kernel clusteringandregularization-based segmentation. While addressing closely related practical problems, these general methodologies may seem very different based on how they are covered in the literature. The differences may...
Using S-PCA we present new approaches to the problem of constrast-invariant appearance detection, specifically eye and face detection.; EigenCuts is a clustering algorithm for finding stable clusters in a dataset. Using a Markov chain perspective, we derive an eigenflow to describe the flow of ...
This work bridges the gap between two popular methodologies for data partitioning:kernel clusteringandregularization-based segmentation. While addressing closely related practical problems, these general methodologies may seem very different based on how they are covered in the literature. The differences may...
A Convex Optimization-Based Coupled Nonnegative Matrix Factorization Algorithm for Hyperspectral and Multispectral Data Fusion. IEEE Trans. Geosci. Remote Sens. 2018, 56, 1652–1667. [Google Scholar] [CrossRef] Li, S.; Dian, R.; Fang, L.; Bioucas-Dias, J.M. Fusing Hyperspectral and ...
Choosing a suitable number of subregions and their respective layouts by a clustering algorithm is essential to design a WDN partition into DMAs. The definition of the number of clusters attempts to take into account some peculiarities of the system (i.e., water demand, pressure distribution, or...