منابع مشابه
Co-regularized Multi-view Spectral Clustering
In many clustering problems, we have access to multiple views of the data each of which could be individually used for clustering. Exploiting information from multiple views, one can hope to find a clustering that is more accurate than the ones obtained using the individual views. Often these different views admit same underlying clustering of the data, so we can approach this problem by lookin...
متن کاملMulti-objective Multi-view Spectral Clustering via Pareto Optimization
Traditionally, spectral clustering is limited to a single objective: finding the normalized min-cut of a single graph. However, many real-world datasets, such as scientific data (fMRI scans of different individuals), social data (different types of connections between people), web data (multi-type data), are generated from multiple heterogeneous sources. How to optimally combine knowledge from ...
متن کاملKernel Cuts: MRF meets Kernel&Spectral Clustering
The log-likelihood energy term in popular model-fitting segmentation methods, e.g. [64, 14, 50, 20], is presented as a generalized “probabilistic” K-means energy [33] for color space clustering. This interpretation reveals some limitations, e.g. over-fitting. We propose an alternative approach to color clustering using kernel K-means energy with well-known properties such as non-linear separati...
متن کاملSpectral Kernel Methods for Clustering
In this paper we introduce new algorithms for unsupervised learning based on the use of a kernel matrix. All the information required by such algorithms is contained in the eigenvectors of the matrix or of closely related matrices. We use two different but related cost functions, the Alignment and the 'cut cost'. The first one is discussed in a companion paper [3], the second one is based on gr...
متن کاملKernel Spectral Clustering and applications
In this chapter we review the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting. KSC represents a least-squares support vector machine based formulation of spectral clustering described by a weighted kernel PCA objective. Just as in the classifier case, the binary clustering model is expressed by a hyperplane i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information Fusion
سال: 2018
ISSN: 1566-2535
DOI: 10.1016/j.inffus.2017.12.002