Global Convergence of EM Algorithm for Mixtures of Two Component Linear Regression

Details

• Alternative Title:
  Clustering and Classification Project Title from Cover
• Creators:
  Kwon, Jeongyeol ; Caramanis, Constantine
• Corporate Creators:
  University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Algorithms
  • Linear regression analysis
  • Statistical analysis
  • Finite element method
  • Maximization
  • Mixed Regression
• Publication/ Report Number:
  D-STOP/2018/145
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP)
• Abstract:
  The Expectation-Maximization algorithm is perhaps the most broadly used algorithm for inference of latent variable problems. A theoretical understanding of its performance, however, largely remains lacking. Recent results established that EM enjoys global convergence for Gaussian Mixture Models. For Mixed Regression, however, only local convergence results have been established, and those only for the high SNR regime. We show here that EM converges for mixed linear regression with two components (it is known not to converge for three or more), and moreover that this convergence holds for random initialization.
• Format:
  PDF
• Funding:
  DTRT13-G-UTC58
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/66277/dot_66277_DS1.pdf
• File Type:
  [PDF - 673.43 KB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha256:938f409a5d119c2dc719985896ee20e8402f603d72799d5157f30b2e983f430f