Incremental spectral clustering by efficiently updating the eigensystem
Semi-supervised learning is a class of machine learning techniques that make use of both labeled and unlabeled data for training - typically a small amount of labeled data with a large amount of unlabeled data.Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data).The algorithm is easy to implement, and outperforms traditional clustering algorithms such as K-means algorithm.
Whereas a large number of dedicated techniques have been recently proposed for static graphs, the design of on-line graph clustering methods tailored for evolving networks is a challenging problem, and much less documented in the literature.How can I efficiently assign a new single point $X_$ to his convenient cluster?Do I have to do the classification from the beginning (destroy all the clusters and apply the algorithm to the data-set $X_0 - X_$), or is there an optimized way to extend to the point $X_$?In this paper, we introduce a new manifold learning algorithm by updating the structure of eigen-problem iteratively.
Incremental spectral decomposition is used in the iterative process and the resulting eigenvectors correspond to the low dimensional embedded coordinates.
Copyright (c) 2012, Ingo Bürk Copyright (c) 2003, Jochen Lenz Copyright (c) 2015, Oliver Woodford All rights reserved.