Thresholded spectral algorithms for sparse approximations

Zheng Chu Guo, Dao Hong Xiang, Xin Guo, DIng Xuan Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

39 Citations (Scopus)


Spectral algorithms form a general framework that unifies many regularization schemes in learning theory. In this paper, we propose and analyze a class of thresholded spectral algorithms that are designed based on empirical features. Soft thresholding is adopted to achieve sparse approximations. Our analysis shows that without sparsity assumption of the regression function, the output functions of thresholded spectral algorithms are represented by empirical features with satisfactory sparsity, and the convergence rates are comparable to those of the classical spectral algorithms in the literature.
Original languageEnglish
Pages (from-to)433-455
Number of pages23
JournalAnalysis and Applications
Issue number3
Publication statusPublished - 1 May 2017


  • learning rate
  • Learning theory
  • sparsity
  • thresholded spectral algorithm

ASJC Scopus subject areas

  • Analysis
  • Applied Mathematics


Dive into the research topics of 'Thresholded spectral algorithms for sparse approximations'. Together they form a unique fingerprint.

Cite this