Unsupervised Learning of Finite Mixture Models

By Mário A. T. Figueiredo, Anil K. Jain. 2002

2,389 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, methodology): “A notable outlier is ANM-MML which spends much time on estimating the MML of the marginal distribution using the algorithm by Figueiredo and Jain (2002).”
EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing (2011, methodology): “(1) Time-frequency window selection (2) Frequency-domain DAL (FDAL) (vi) Gaussian mixture models (GMM), 9 methods [35–37])”
Robust data clustering (2003, methodology): “The evidence accumulation technique was also applied to 285 prototypes extracted from the new diagnostic Breast Cancer database (569 patterns, 30 numeric attributes), by using the same noise removal technique, N = 100, and k ∈ [2, 10]; the combined data partition contains 2 clusters, with a correct ”
Scream and gunshot detection and localization for audio-surveillance systems (2007, methodology): “Each binary classifier is trained separately with the samples of the respective classes (gunshot and noise, or scream and noise), using the Figueiredo and Jain algorithm [11].”
Feature Selection for Unsupervised Learning (2004, background): “Recently, Figueiredo and Jain (2002) presented an efficient algorithm which integrates estimation and model selection for finding the number of clusters using minimum message length (a penalty method).”
Set Partitioning via Inclusion-Exclusion (2009, neutral): “, [3, 17, 27, 37]).”
Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates (2009, methodology): “In future implementations, model selection techniques for EM (such as [7]) could be used.”
Maximin effects in inhomogeneous large-scale data (2014, background): “…Huber (1964, 1973)], timevarying coefficients models [Cai, Fan and Li (2000), Fan and Zhang (1999), Hastie and Tibshirani (1993)], mixture models [Aitkin and Rubin (1985), Figueiredo and Jain (2002), McLachlan and Peel (2000)] and change-point estimation [Carlstein, Müller and Siegmund (1994)].”
Model learning for robot control: a survey (2011, background): “In cases where labeled data are hard to obtain or not available, unsupervised learning can be employed instead (Sanger 1989; Figueiredo and Jain 2002).”
Statistical shape analysis: clustering, learning, and testing (2005, background): “Another possibility is to use human supervision in selecting k. Ideas presented in [5] can also used for unsupervised clustering.”
Unsupervised K-Means Clustering Algorithm (2020, result): “This situation is similar as the formula in Figueiredo and Jain [32].”
Combining multiple clusterings using evidence accumulation (2005, methodology): “By using the mixture decomposition method in [25], the data set is decomposed into 10 Gaussian components.”
Finite mixture models and model-based clustering (2010, background): “Many different initialization procedures have been suggested in the literature (for an overview, see [40] and [78]) but no method uniformly outperforms the others.”
Data clustering using evidence accumulation (2002, methodology): “(b) Decomposition of (a) into a mixture of gaussians using the method in [3].”