Support Vector Clustering

By Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Naumovich Vapnik. 2002

1,579 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Dacheng Tao
2. Yi Yang
3. Yoshua Bengio
4. Xiaogang Wang
5. Feiping Nie
6. Trevor Darrell
7. Sergey Levine
8. Qiang Yang

Citations in the corpus

• Surpassing Human-Level Face Verification Performance on LFW with GaussianFace (2014, methodology): “After each data point finds its corresponding stable equilibrium point, we can employ a complete graph [4, 26] to assign cluster labels to data points with the stable equilibrium points.”
• Automated Machine Learning: From Principles to Practices (2018, background): “The past decades have witnessed the rapid development of machine learning techniques, evolving from Support Vector Machine (SVM) [9, 30] that is designed for handling basic classification task, to Convolutional Neural Network (CNN) [54, 75] that can well handle image data in computer vision tasks,…”
• Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review (2020, methodology): “However, KNN is less computationally intensive and easy to implement than SVM hence it is mostly used in the classification of multi-class data [15].”
• A Review on Modal Clustering (2016, background): “A further approach having a connection with modal clustering is support vector clustering (Ben-Hur et al., 2001).”
• A novel kernelized fuzzy C-means algorithm with application in medical image segmentation (2004, methodology): “And this 'kernel method' has also been applied to unsupervised clustering [26] [20, 21].”
• Flexible Kernel Memory (2010, background): “The memory system introduced here takes advantage of developments introduced in Support Vector Machine (SVM) [37], Least-Square SVM [38] and Support Vector Clustering [39], where kernel functions enable data handling in higher feature spaces for richer boundaries, yet do so efficiently and cheaply.”
• Survey of clustering algorithms (2005, methodology): “presented a new clustering algorithm, SVC, in order to find a set of contours used as the cluster boundaries in the original data space [31], [32].”
• Robust continuous clustering (2017, background): “Significant advances in the last two decades include spectral clustering (2–4), generalizations of classic center-based methods (5, 6), mixture models (7, 8), mean shift (9), affinity propagation (10), subspace clustering (11–13), nonparametric methods (14, 15), and feature selection (16–20).”
• Hyperspectral Image Classification With Independent Component Discriminant Analysis (2011, methodology): “kernel Fisher discriminant analysis [26], support vector clustering [27], and the regularized AdaBoost algorithm [28]) have been proposed in the machine learning community, providing”
• Kernel Methods in Finance (2008, methodology): “, 2002), SVMs for clustering (Tax and Duin, 1999; Ben-Hur et al., 2001; Yang et al., 2002b), and SVMs for multi-class classification (Rifkin and Klautau, 2004).”
• Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers (2008, methodology): “We adopt the support vector clustering (SVC) algorithm, first proposed by Ben-Hur et al. (2001), to cluster the row vectors of the CPC loadings to indicate which features have similar contributions to each of the CPC loadings.”
• Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine (2019, background): “that depend on continuous or categorical predictor variable [96].”
• Support Vector Machines with Applications (2006, methodology): “. See [14] for further details. Many other kernel methods have been proposed in the literature. To name a few, there are extensions to PCA [70], Fisher discriminant analysis [6, 44], cluster analysis [8, 46], partial least squares [66], time series analysis [50], multivariate density estimation [49,”
• Clustering ensembles: models of consensus and weak partitions (2005, background): “For example , Support Vector Clustering [5] seeks spherical clusters after the kernel transformation that corresponds to more complex cluster shapes in the original pattern space.”