For a clustering problem with n samples, it needs to compute the eigenvectors of the graph Laplacian with O(n~3) time complexity. To address this problem, we propose a novel method called anchor-based spectral clustering(ASC) by employing anchor points of data. Specifically, m(m鈮 ) anchor...
2021. All Rights Reserved.The spectral clustering (SC) method has a good clustering effect on arbitrary structure datasets because of its solid theoretical basis. However, the required time complexity is high, thus limiting the application of SC in big datasets. To reduce time complexity, we prop...
This is preliminary, but fully functional, implementation of the Spectral Clustering algorithm for the WEKA framework. WEKA is an Open Source Knowledge Discovering and Data Mining system developed in Java by the University of Waikato in New Zealand It offers many algorithms and tools commonly availabl...
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 ...
attribute to reconstruct the data set. Secondly, the voting mechanism is used to reflect the consistency of the clustering results and give the category of each spectrum. At the same time, rough sets are defined to trace the characteristics of each spectrum, and the reliability of the classifica...
Principal component analysis (PCA) was performed on the preprocessed spectral data, and the score map (PC1: 98.44%, PC2: 3.5%, PC3: 0.14%) obtained according to the principal component analysis showed that the samples had obvious clustering and were in two The samples can be separated from ...
two main problems to be solved: 1) spectral clustering methods consist of two successive optimization stages-spectral embedding and spectral rotation, which may not lead to globally optimal solutions, 2) and it is known that spectral methods are time-consuming with very high computational complexity...
[56,57] adopted an enhanced K-medoids clustering approach to obtain the corresponding linear spectral line intensity by obtaining the central point. The spectral distance was employed in place of the conventional Euclidean distance in accordance with the changing characteristics of spectrum data. The ...
In this work we present an efficient and simple technique for spatio-temporal segmentation that is based on a low-rank spectral clustering algorithm. The complexity of graph based spatio-temporal segmentation is dominated by the size of the graph, which is proportional to the number of pixels in...
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...