Incremental spectral clustering by efficiently updating the eigensystem
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.
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).
I was also a member of the Image Formation and Processing Group(IFP) at the Beckman Institute for Advanced Science and Technologies at UIUC.
Say I used spectral clustering to cluster a data-set $D$ of points $X_0 - X_n$ into a number $C$ of clusters.
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It is closely related to the theory of network flow problems.We then derive a novel spectral clustering algorithm called Incremental Approximate Spectral Clustering (IASC).