Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2014 Mar 1:38:132-141.
doi: 10.1016/j.patrec.2013.11.021.

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

Mehmet Gönen  1
Affiliations

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

Mehmet Gönen. Pattern Recognit Lett. .

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.

Keywords: Automatic relevance determination; Dimensionality reduction; Multilabel learning; Semi-supervised learning; Supervised learning; Variational approximation.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Coupled dimensionality reduction and classification for supervised multilabel learning.
Figure 2
Figure 2
Comparison of algorithms on Emotions data set.
Figure 3
Figure 3
Comparison of algorithms on Scene data set.
Figure 4
Figure 4
Comparison of algorithms on TMC2007 data set.
Figure 5
Figure 5
Comparison of algorithms on Yeast data set.
Figure 6
Figure 6
Two-dimensional embeddings obtained by (a) MLDA and (b) BSML on Emotions data set.
See this image and copyright information in PMC

References

    1. Albert JH, Chib S. Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association. 1993;88:669–679.
    1. Beal MJ. Ph D thesis. The Gatsby Computational Neuroscience Unit; University College London: 2003. Variational algorithms for approximate Bayesian inference.
    1. Biem A, Katagiri S, Juang B-H. Pattern recognition using discriminative feature extraction. IEEE Transactions on Signal Processing. 1997;45:500–504.
    1. Boutell MR, Luo J, Shen X, Brown CM. Learning multi-label scene classification. Pattern Recognition. 2004;37:1757–1771.
    1. Chapelle O, Vapnik V, Bousquet O, Mukherjee S. Choosing multiple parameters for support vector machines. Machine Learning. 2002;46:131–159.

LinkOut - more resources