Among various vertex clustering approaches, spectral clustering is one of the most popular methods because it is easy to implement while often outperforming more traditional clustering algorithms. However, there are two inherent model selection problems in spectral clustering, namely estimating both the ...
Computationally expensive for large datasets — This is because eigenvalues and eigenvectors need to be computed and then we have to do clustering on these vectors. For large, dense datasets, this may increase time complexity quite a bit. In this blog I have explained the math behind spectral c...
Furthermore, the eigenvalue decomposition, a key step in spectral clustering, entails cubic computational complexity. This paper incorporates linear embedding into the objective function of spectral clustering and proposes a direct method to solve the indicator matrix. Moreover, our method achieves a ...
The proposed algorithm has much lower time complexity than that of the standard interior-point-based SDP solvers. Experimental results on both the UCI data sets and real-world image data sets demonstrate that: 1) compared with the state-of-the-art spectral clustering methods, the proposed ...
However, the traditional spectral clustering lacks the temporal expansion on the algorithm and limited in its applicability to large-scale problems due to its high computational complexity. This paper presents a parallel spatiotemporal spectral clustering based on multiple acceleration solutions to make ...
1. A method of clustering data, comprising the steps of: (a) providing a set of first matrices relating to at least three types of objects, each respective first matrix comprising a set of features and a set of respective relations with respect to other objects; (b) automatically generating...
In this paper, we propose a framework of hierarchical modeling of a complex network system, based on a recursive unsupervised spectral clustering method. The hierarchical model serves the purpose of facilitating the management of complexity in the analysis of real-world critical infrastructures. We ...
Spectral clustering (SC) is drawing more and more attention due to its effectiveness in unsupervised learning. However, all of these methods still have limitations. First, the method is not suitable for large-scale problems due to its high computational complexity. Second, the neighborhood weighted...
We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orientations are unknown. We propose a simple and low-complexity clustering algorithm based on thresholding the correlations...
This method can be especially useful for clustering methods of complexity strongly increasing with the size of the data sample,like in case of typical spectral clustering. Experiments were performed showing that in fact the clustering and merging the subsets yields clusters close to clustering the ...