Spectral Clustering with Local Projection Distance Measurement
نویسندگان
چکیده
منابع مشابه
Constrained Spectral Clustering with Distance Metric Learning
Spectral clustering is a flexible clustering technique that finds data clusters in the spectral embedding space of the data. It doesn’t assume convexity of the shape of clusters, and is able to find non-linear cluster boundaries. Constrained spectral clustering aims at incorporating user-defined pairwise constraints in to spectral clustering. Typically, there are two kinds of pairwise constrain...
متن کاملOptimal 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 کاملFast Constrained Spectral Clustering and Cluster Ensemble with Random Projection
Constrained spectral clustering (CSC) method can greatly improve the clustering accuracy with the incorporation of constraint information into spectral clustering and thus has been paid academic attention widely. In this paper, we propose a fast CSC algorithm via encoding landmark-based graph construction into a new CSC model and applying random sampling to decrease the data size after spectral...
متن کاملSpectral Clustering of the Google Distance
The World Wide Web provides a huge amount of documents reflecting (parts of) the humans’ view of the world and its history. Recently, Cilibrasi and Vitányi have suggested a way to make this data usable for supervised and unsupervised learning, by employing the popular Google search engine in order to define a distance function on pairs of terms. In this work, we propose to apply spectral cluste...
متن کاملSpectral Clustering Based on Local PCA
We propose a spectral clustering method based on local principal components analysis (PCA). After performing local PCA in selected neighborhoods, the algorithm builds a nearest neighbor graph weighted according to a discrepancy between the principal subspaces in the neighborhoods, and then applies spectral clustering. As opposed to standard spectral methods based solely on pairwise distances be...
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: Mathematical Problems in Engineering
سال: 2015
ISSN: 1024-123X,1563-5147
DOI: 10.1155/2015/829514