Kernel spectral clustering of large dimensional data
نویسندگان
چکیده
منابع مشابه
Optimal Data Projection for Kernel Spectral Clustering
Spectral clustering has taken an important place in the context of pattern recognition, being a good alternative to solve problems with non-linearly separable groups. Because of its unsupervised nature, clustering methods are often parametric, requiring then some initial parameters. Thus, clustering performance is greatly dependent on the selection of those initial parameters. Furthermore, tuni...
متن کاملKernel Spectral Clustering for Big Data Networks
This paper shows the feasibility of utilizing the Kernel Spectral Clustering (KSC) method for the purpose of community detection in big data networks. KSC employs a primal-dual framework to construct a model. It results in a powerful property of effectively inferring the community affiliation for out-of-sample extensions. The original large kernel matrix cannot fitinto memory. Therefore, we sel...
متن کاملAgglomerative hierarchical kernel spectral clustering for large scale networks
We propose an agglomerative hierarchical kernel spectral clustering (AH-KSC) model for large scale complex networks. The kernel spectral clustering (KSC) method uses a primal-dual framework to build a model on a subgraph of the network. We exploit the structure of the projections in the eigenspace to automatically identify a set of distance thresholds. These thresholds lead to the different lev...
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 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: Electronic Journal of Statistics
سال: 2016
ISSN: 1935-7524
DOI: 10.1214/16-ejs1144