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Image segmentation by selecting eigenvectors based on extended information entropy
IET Image Processing, 2023 For spectral clustering algorithm, the quality of eigenvectors of graph affinity matrix is very important for the clustering result. So, how to obtain high‐quality eigenvectors is crucial.
Daming Zhang, Xueyong Zhang, Huayong Liu
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SACOC: A spectral-based ACO clustering algorithm [PDF]
, 2014 The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning
Otero, Fernando E. B. +7 more
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PolSAR Image Classification Method Based on Markov Discriminant Spectral Clustering
Leida xuebao, 2019 Due to the existing spectral clustering methods have low accuracy for PolSAR image classification, a Markov-based Discriminative Spectral Clustering(MDSC) method is proposed, which has the characteristics of low-rank and sparse decomposition.
ZHANG Xiangrong +4 more
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Density Gain-Rate Peaks for Spectral Clustering
IEEE Access, 2021 Clustering has been troubled by varying shapes of sample distributions, such as line and spiral shapes. Spectral clustering and density peak clustering are two feasible techniques to address this problem, and have attracted much attention from academic ...
Jiexing Liu, Chenggui Zhao
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A survey of kernel and spectral methods for clustering [PDF]
, 2007 Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods ...
Masulli, F. +11 more
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Scalable Spectral Clustering With Nyström Approximation: Practical and Theoretical Aspects
IEEE Open Journal of Signal Processing, 2020 Spectral clustering techniques are valuable tools in signal processing and machine learning for partitioning complex data sets. The effectiveness of spectral clustering stems from constructing a non-linear embedding based on creating a similarity graph ...
Farhad Pourkamali-Anaraki
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On spectral clustering, informativeness and seriation
, 2022 Brockmeier, Austin J.This thesis studies spectral clustering and seriation, which have very similar relaxed objective functions. We analyzed these problems from the standpoint of informativeness, an unsupervised measure of the similarity within a data ...
Riaz, Bilal
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Spectral camera clustering [PDF]
2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, 2009 We propose an algorithm for clustering large sets of images of a scene into smaller subsets covering different parts of the scene suitable for 3D reconstruction. Unlike the canonical view selection of [13], we do not focus only on the visibility information, but introduce an alternative similarity measure which takes into account the relative camera ...
Alexander Ladikos +2 more
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Newtonian Spectral Clustering [PDF]
, 2009 In this study we propose a systematic methodology for constructing a sparse affinity matrix to be used in an advantageous spectral clustering approach. Newton's equations of motion are employed to concentrate the data points around their cluster centers, using an appropriate potential.
Blekas, K. +2 more
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Constrained spectral clustering based methodology for intentional controlled islanding of large-scale power systems [PDF]
, 2015 Intentional controlled islanding is an effective corrective approach to minimise the impact of cascading outages leading to large-area blackouts. This study proposes a novel methodology, based on `constrained spectral clustering', that is computationally
Quiros-Tortos, Jairo +9 more
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