A probabilistic framework for semi-supervised clustering

By Sugato Basu, Mikhail Bilenko, Raymond J. Mooney. Knowledge Discovery and Data Mining, 2004

887 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/01d5bf24c0b35d9a234d534bf69924fa16201dee

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In Artificial Intelligence

Citations in the corpus

Heterogeneous Transfer Learning for Image Clustering via the SocialWeb (2009, background): “performance, such as supervised clustering (Finley and Joachims, 2005), semi-supervised clustering (Basu et al., 2004), self-taught clustering (Dai et al.”
Offline/realtime traffic classification using semi-supervised learning (2007, methodology): “This method, referred to as semisupervised learning [1, 4, 6], has received considerable attention, recently, in the machine learning community.”
Clustering with Constraints: Feasibility Issues and the k-Means Algorithm (2005, methodology): “We can divide previous work on clustering under constraints into two types: 1) Where the constraints help the algorithm learn a distortion/distance/objective function [4, 15] and 2) Where the constraints are used as “hints” to guide the algorithm to a useful solution [18, 19].”
ArnetMiner: extraction and mining of academic social networks (2008, methodology): “According to HMRF, the conditional distribution of the researcher labels y given the observations x (papers) is 1 P (y|x)= exp(-”
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”
Learning to recognize objects in egocentric activities (2011, methodology): “al [3] to cluster the super-pixels into multiple words using the mentioned similarity and dissimilarity constraints.”
An Active Learning Approach to Coreference Resolution (2015, methodology): “, 2004a] and [Basu et al., 2004b] and we will show that our approach outperforms”
Semi-Supervised Dimensionality Reduction (2007, methodology): “…of the SSDR algorithms on a broad range of data sets, including six UCI data sets [4], i.e. balance, ionosphere, iris, sonar, soybean and wine, YaleB facial image data set [8], and three text data sets derived from 20-Newsgroup [2], i.e. News-Different- 300, News-Similar-300 and News-Same-300.”
Self-taught clustering (2008, methodology): “Basu et al. (2004)generalized the previous semi-supervised clustering algorithms and proposed a probabilistic framework based on hidden Markov random .elds that combines the constraints and clustering distortionmeasuresinageneralframework.”
Discovering Latent Domains for Multisource Domain Adaptation (2012, background): “Improvements to this basic algorithm were considered in [17, 18].”
Information-theoretic metric learning (2006, methodology): “Instead, we compare our method to the semi-supervised clustering algorithm HMRF-KMeans (Basu et al., 2004).”
Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds (2011, methodology): “This body of research includes various approaches such as classical Fisher’s discriminant analysis (FDA) [3], the large family of methods known as metric learning (ML) [4–12], the family of sufficient dimension reduction (SDR) algorithms [13–20], and the method proposed by Bair et al.”
Integrating constraints and metric learning in semi-supervised clustering (2004, methodology): “Our formulation, on the other hand, has an underlying generative model based on Hidden Markov Random Fields (see (Basu et al., 2004) for a detailed analysis).”