Semi-supervised graph clustering: a kernel approach

By Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney. Machine-mediated learning, 2005

525 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3215cbbf6c328b0a59058816fbfe51424febb156

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Statistical and Nonlinear Physics

Citations in the corpus

Discovering Latent Domains for Multisource Domain Adaptation (2012, background): “there is ample literature on adding constraints to the k-means objective via constraints or penalties [16, 18].”
Normalized Cut Loss for Weakly-Supervised CNN Segmentation (2018, background): “Due to importance of lowlevel regularizers for segmentation and computer vision in general, there are many choices available in the literature [19, 11, 9, 43, 23, 34, 39, 30, 15, 46].”
A survey of kernel and spectral methods for clustering (2008, unknown): “[42].”
Semi-Supervised Kernel Mean Shift Clustering (2014, methodology): “More recently, Kulis et al. proposed semi-supervised kernel k-means (SSKK) algorithm [24], that constructs a kernel matrix by adding the input similarity matrix to the constraint penalty matrix.”
Active co-analysis of a set of shapes (2012, methodology): “Many existing techniques directly modify the similarity matrix according to the given constraints and then perform spectral clustering [Kamvar et al. 2003; Kulis et al. 2005; Lu and Carreira-Perpinán 2008].”
An Active Learning Approach to Coreference Resolution (2015, background): “However, we should point out that our work also has connections with constrained Spectral Clustering [Kamvar et al., 2003], Constrained K-means [Wagstaff et al., 2001], Semi-supervised clustering [Kulis et al., 2005].”
Supervised pattern classification based on optimum‐path forest (2009, background): “The object is defined by the optimum-path forest rooted at the pixels of the internal marker.”
Semi-supervised Kernel Metric Learning Using Relative Comparisons (2016, methodology): “For instance, Kulis et al. [2009a] present a uniﬁed framework for semi-supervised vector and graph clustering using spectral clustering and kernel learning.”
Symmetric Nonnegative Matrix Factorization for Graph Clustering (2012, background): “All the cases in Table 1 can be reduced to the following trace maximization form, assuming the similarity matrix A is constructed correspondingly [5, 13]:”
Secrets of GrabCut and Kernel K-Means (2015, methodology): “[11, 17] first observed the equivalence between kernel k-means and popular spectral clustering criteria.”
A Survey of Optimization Methods From a Machine Learning Perspective (2019, unknown): “including classification tasks [70], [71]; regression tasks [72]; clustering tasks [73], [74]; and dimensionality reduction tasks [75], [76].”
Hierarchical graph embedding in vector space by graph pyramid (2017, methodology): “to the processing method of this matrix, the node clustering approaches can be divided into: optimization of graph clustering objectives [28], spectral methods [29], kernel-based methods [30] and multi-level methods [28, 31].”
Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification (2009, background): “Another direction is semi-supervised clustering [1] [2] [3] [4] [5], which incorporates pairwise constraints, e.”