Accelerated Gradient and Block-Wise Gradient Methods for Big Data Factorization

No abstract is available for this paper.

• Publication year

  2018

• Venue

  Canadian Conference on AI

• Publication date

  2018-05-08

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1007/978-3-319-89656-4_20
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Armadillo: a template-based C++ library for linear algebra

  2016cited by this paper
• Finding Low-rank Solutions to Matrix Problems, Efficiently and Provably

  2016cited by this paper
• Low-rank Solutions of Linear Matrix Equations via Procrustes Flow

  2015cited by this paper
• Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees

  2015cited by this paper
• Normalized Iterative Hard Thresholding for Matrix Completion

  2013cited by this paper
• Accelerated gradient methods for nonconvex nonlinear and stochastic programming

  2013cited by this paper
• Low-rank matrix completion using alternating minimization

  2012cited by this paper
• ADMiRA: Atomic Decomposition for Minimum Rank Approximation

  2009cited by this paper
• A Singular Value Thresholding Algorithm for Matrix Completion

  2008cited by this paper
• Boosted decision trees as an alternative to artificial neural networks for particle identification

  2004cited by this paper
• A method for unconstrained convex minimization problem with the rate of convergence o(1/k^2)

  1983cited by this paper

• No citing papers are available for this paper.