Fetching the paper…
Reading the bibliography…
We study the convergence of the Expectation-Maximization (EM) algorithm for mixtures of linear regressions with an arbitrary number $k$ of components.
On the convergence properties of the em algorithm
CF Jeff Wu et al · 1983
Earlier work this paper cites.
Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin · 2010
Earlier work this paper cites.
Spectral experts for estimating mixtures of linear regressions
Arun Tejasvi Chaganty and Percy Liang · 2013
Earlier work this paper cites.
Alternating minimization for mixed linear regression
Xinyang Yi, Constantine Caramanis, and Sujay Sanghavi · 2014
Earlier work this paper cites.
Provable tensor methods for learning mixtures of generalized linear models
Hanie Sedghi, Majid Janzamin, and Anima Anandkumar · 2014
Earlier work this paper cites.
A convex formulation for mixed regression with two components: Minimax optimal rates
Yudong Chen, Xinyang Yi, and Constantine Caramanis · 2014
Earlier work this paper cites.
Regularized em algorithms: A unified framework and statistical guarantees
Xinyang Yi and Constantine Caramanis · 2015
Earlier work this paper cites.
Local maxima in the likelihood of gaussian mixture models: Structural results and algorithmic consequences
Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J Wainwright, and Michael I Jordan · 2016
Cited alongside, same era.
Xinyang Yi, Constantine Caramanis, and Sujay Sanghavi · 2016
Cited alongside, same era.
Mixed linear regression with multiple components
Kai Zhong, Prateek Jain, and Inderjit S Dhillon · 2016
Cited alongside, same era.
Statistical guarantees for the em algorithm: From population to sample-based analysis
Sivaraman Balakrishnan, Martin J Wainwright, Bin Yu, et al · 2017
Cited alongside, same era.
Ten steps of em suffice for mixtures of two gaussians
Constantinos Daskalakis, Christos Tzamos, and Manolis Zampetakis · 2017
Cited alongside, same era.
On learning mixtures of well-separated gaussians
Oded Regev and Aravindan Vijayaraghavan · 2017
Later among the works it cites.
Statistical convergence of the em algorithm on gaussian mixture models
Ruofei Zhao, Yuanzhi Li, and Yuekai Sun · 2018
Later among the works it cites.
A convex program for mixed linear regression with a recovery guarantee for well-separated data
Paul Hand and Babhru Joshi · 2018
Later among the works it cites.
Learning mixtures of linear regressions with nearly optimal complexity
Yuanzhi Li and Yingyu Liang · 2018
Later among the works it cites.
Global convergence of the em algorithm for mixtures of two component linear regression
Jeongyeol Kwon, Wei Qian, Constantine Caramanis, Yudong Chen, and Damek Davis · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Convergence of gradient em on multi-component mixture of gaussians
Bowei Yan, Mingzhang Yin, and Purnamrita Sarkar · 2017
Cited alongside, same era.
Convex and nonconvex formulations for mixed regression with two components: Minimax optimal rates
Yudong Chen, Xinyang Yi, and Constantine Caramanis · 2017
Cited alongside, same era.
Jason M Klusowski, Dana Yang, and WD Brinda · 2019
Closest in time.
Chime: Clustering of high-dimensional gaussian mixtures with em algorithm and its optimality
T Tony Cai, Jing Ma, Linjun Zhang, et al · 2019
Closest in time.