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 properties of the matrix of transition probabilities, from which we derive eigenflows along ...
clustering can work together and why this is useful. Our joint energy combines standard regularization, e.g. MRF potentials, and kernel clustering criteria like normalized cut. Complementarity of such terms is demonstrated in many applications using our bound optimizationKernel Cutalgorithm for the ...
Rational partitioning of spectral feature space for effective clustering of massive spectral image data Yusei Ito Yasuo Takeichi Kanta Ono Scientific Reports(2024) Efficiency improvement of spin-resolved ARPES experiments using Gaussian process regression ...
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 ...
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...