Semi-Supervised Clustering with User Feedback

By David A. Cohn, R. Caruana, Andrew McCallum. 2003

305 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cc86aa7d81cfac9aaec4b095e817ce7bb1f1ef78

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In Artificial Intelligence

Citations in the corpus

Active Semi-Supervision for Pairwise Constrained Clustering (2004, background): “[8] have proposed iterative userfeedback to acquire constraints, but it was not an active learning algorithm.”
Interactive and interpretable machine learning models for human machine collaboration (2015, background): “Active learning [37, 128, 102], semi-supervised learning [36, 15], interactive machine learning [18, 89, 7] and a set of application-specific systems [119, 125, 42, 131, 5, 14, 7] are some examples of work that enable communication from humans to machines while improving the way to represent machine”
A probabilistic framework for semi-supervised clustering (2004, methodology): “after such parameterization DKL is no longer a Bregman divergence over probability distributions, which is undesirable since convergence is no longer guaranteed for the algorithm described in [13].”
An Active Learning Approach to Coreference Resolution (2015, methodology): “Metric learning given constraints and constrained pairwise clustering have both been well-studied in machine learning [Cohn et al., 2003; Xing et al., 2003].”
Classes of Kernels for Machine Learning: A Statistics Perspective (2002, background): “Unfortunately, those pairwise relations are often determined in a subjective way [2] or with significant uncertainty [4].”
Power to the People: The Role of Humans in Interactive Machine Learning (2014, methodology): “Incited by this experience, Caruana et al. went on to develop novel feedback techniques for more interactively incorporating domain expert knowledge into the distance metric used for clustering (Cohn et al. 2003, Caruana et al. 2006).”
Integrating constraints and metric learning in semi-supervised clustering (2004, methodology): “Several distance measures have been used for metric-based semi-supervised clustering including Euclidean distance trained by a shortest-path algorithm (Klein et al., 2002), string-edit distance learned using Expectation Maximization (EM) (Bilenko & Mooney, 2003), KL divergence adapted using gradient”
Learning a Distance Metric from Relative Comparisons (2003, background): “While [10] does not change the distance metric, [2] uses gradient descent to adapt a parameterized distance metric according to user feedback.”