Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational approximation algorithm to learn the proposed probabilistic model. We then extend the proposed method to find intrinsic dimensionality of the projected subspace using automatic relevance determination and to handle semi-supervised learning using a low-density assumption. We perform supervised learning experiments on four benchmark multilabel learning data sets by comparing our method with baseline linear dimensionality reduction algorithms. These experiments show that the proposed approach achieves good performance values in terms of hamming loss, average AUC, macro F1, and micro F1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis. We also show the effectiveness of our approach in finding intrinsic subspace dimensionality and semi-supervised learning tasks.

Original languageEnglish (US)
Pages (from-to)132-141
Number of pages10
JournalPattern Recognition Letters
Volume38
Issue number1
DOIs
StatePublished - Mar 1 2014

    Fingerprint

Keywords

  • Automatic relevance determination
  • Dimensionality reduction
  • Multilabel learning
  • Semi-supervised learning
  • Supervised learning
  • Variational approximation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this