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 ...
Spectral clustering is a helpful technique for clustering non-convex data, which extends the clustering to data with multiple partial views. Still, it has a higher running time thanks to cubic time complexity, while extending spectral embeddings to unseen samples is non-trivial and prevents model ...
Despite its good performance, spectral clustering is often limited in its application for large-scale problems due to its high computational complexity27. To address this challenge, the spectral clustering using Nyström approximation is presented to reduce the computational cost of the matrix ...
As a leading graph clustering technique, spectral clustering is one of the most widely used clustering methods that captures complex clusters in data. However, some of its deficiencies, such as the high computational complexity in eigen decomposition and the guidance without supervised information, limi...
After all these steps, the algorithm finishes the clustering by assigning class labels to all data points using information of the selected exemplars. Tabatabei et al. devised a new graph clustering method which was an algorithm to maximize normalized association with good time-complexity [22]. ...
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 clusteringDcut 图像阂值分钊,多阂值,谱聚类,DcutThe thresholding is an important form of image segmentation and is used in many applications that involve image processing and object recognition. hhus, it is crucial to how to acquire a threshold of image segmentation. A novelmultilevel ...
clustering is generously illustrative to have an elaborate overview of the cluster formation, which acts as a huge advantage to realize the similarity between data points in sub-clusters, it comes at the cost of higher time and space complexity (Garima et al. [42]) than partitional clustering....
A novel approach is presented for synthetic aperture radar (SAR) image segmentation. By incorporating the advantages of maximally stable extremal regions (MSER) algorithm and spectral clustering (SC) method, the proposed approach provides effective and robust segmentation. First, the input image is tra...
In this Section, the complexity analysis is conducted on our framework as well as the large scale spectral clustering. To handle the large scale data, the sparse structures such as sparse matrices are used to store the information. While for the eigenvectors (e.g. vertex, hyperedge, landmark...