Sparse additive machine

Tuo Zhao, Han Liu

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations

Abstract

We develop a high dimensional nonparametric classification method named sparse additive machine (SAM), which can be viewed as a functional version of support vector machine (SVM) combined with sparse additive modeling. the SAM is related to multiple kernel learning (MKL), but is computationally more efficient and amenable to theoretical analysis. In terms of computation, we develop an efficient accelerated proximal gradient descent algorithm which is also scalable to large datasets with a provable O(1/k2) convergence rate, where k is the number of iterations. In terms of theory, we provide the oracle properties of the SAM under asymptotic frameworks. Empirical results on both synthetic and real data are reported to back up our theory.

Original languageEnglish (US)
Pages (from-to)1435-1443
Number of pages9
JournalJournal of Machine Learning Research
Volume22
StatePublished - Jan 1 2012
Event15th International Conference on Artificial Intelligence and Statistics, AISTATS 2012 - La Palma, Spain
Duration: Apr 21 2012Apr 23 2012

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Statistics and Probability
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Sparse additive machine'. Together they form a unique fingerprint.

Cite this