Semi-supervised Robust Feature Selection with &#8467;<sub><i>q</i></sub>-Norm Graph for Multiclass Classification

LUO Hui; HAN Jiqing

doi:10.1049/cje.2021.05.003

Volume 30 Issue 4

Jul. 2021

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2021 > 30(4): 611-622

LUO Hui and HAN Jiqing, “Semi-supervised Robust Feature Selection with ℓq-Norm Graph for Multiclass Classification,” Chinese Journal of Electronics, vol. 30, no. 4, pp. 611-622, 2021, doi: 10.1049/cje.2021.05.003

Citation:

LUO Hui and HAN Jiqing, “Semi-supervised Robust Feature Selection with ℓ_q-Norm Graph for Multiclass Classification,” Chinese Journal of Electronics, vol. 30, no. 4, pp. 611-622, 2021, doi: 10.1049/cje.2021.05.003

Citation:

PDF( 1130 KB)

Semi-supervised Robust Feature Selection with ℓ_q-Norm Graph for Multiclass Classification

doi: 10.1049/cje.2021.05.003

LUO Hui,
HAN Jiqing^,

Harbin Institute of Technology, Harbin 150001, China

Funds:

This work is supported by National Science Foundation of China (No.U1736210) and National Key Research and Development Program of China (No.2017YFB1002102).

Received Date: 2018-11-23
Available Online: 2021-07-19
Publish Date: 2021-07-05

Abstract

Abstract

Flexible manifold embedding (FME) is a semi-supervised dimension reduction framework. It has been extended into feature selection by using different loss functions and sparse regularization methods. However, these kind of methods used the quadratic form of graph embedding, thus the results are sensitive to noise and outliers. In this paper, we propose a general semisupervised feature selection model that optimizes an ℓ_q-norm of FME to decrease the noise sensitivity. Compare to the fixed parameter model, the ℓ_q-norm graph brings flexibility to balance the manifold smoothness and the sensitivity to noise by tuning its parameter. We present an efficient iterative algorithm to solve the proposed ℓ_q-norm graph embedding based semi-supervised feature selection problem, and offer a rigorous convergence analysis. Experiments performed on typical image and speech emotion datasets demonstrate that our method is effective for the multiclass classification task, and outperforms the related state-of-the-art methods.
- Feature selection,
- Semi-supervised learning,
- Flexible manifold embedding

FullText(HTML)

References(31)

References

P.P. Mu, S.Y. Zhang, X. Pan, et al., "A Unified Feature Representation and Learning Framework for 3D Shape", Chinese Journal of Electronics, Vol.28, No.5, pp.993-999, 2019.

Z.F. Wang, J.Q. Zhen, Y.C. Li, et al., "Multi-feature multimodal biometric recognition based on quaternion locality preserving projection", Chinese Journal of Electronics, Vol.28, No.4, pp.789-796, 2019.

Y. Han, K. Park and Y.K. Lee, "Confident wrapper-type semi-supervised feature selection using an ensemble classifier", Proc. of The 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, pp.4581-4586, 2011.

S.Z. Lv, H.X. Jiang, L. Zhao, et al., "Manifold based Fisher method for semisupervised feature selection", Proc. of the International Conference on Fuzzy Systems and Knowledge Discovery, Shenyang, China, pp.664-668, 2013.

J. J. Y. Wang, J. Yao and Y.J. Sun, "Semi-supervised local-learning-based feature selection", Proc. of the International Joint Conference on Neural Networks, Beijing, China, pp.1942-1948, 2014.

I. Guyon and A. Elisseeff, "An introduction to variable and feature selection", Journal of Machine Learning Research, Vol.3, No.6, pp.1157-1182, 2003.

Y. Liu, F.P. Nie, J.G. Wu, et al., "Efficient semi-supervised feature selection with noise insensitive trace ratio criterion", Neurocomputing, Vol.105, No.3, pp.12-18, 2013.

K. Benabdeslem and M. Hindawi, "Efficient semi-supervised feature selection:Constraint, relevance, and redundancy", IEEE Transactions on Knowledge and Data Engineering, Vol.26, No.5, pp.1131-1143, 2014.

H. Barkia, H. Elghazel and A. Aussem, "Semi-supervised feature importance evaluation with ensemble learning", Proc. of IEEE International Conference on Data Mining, Brussels, Belgium, pp.31-40, 2012.

Z.L. Xu, I. King, M. R. Lyu, et al., "Discriminative semisupervised feature selection via manifold regularization", IEEE Transactions on Neural Networks, Vol.21, No.7, pp.1033-1047, 2010.

K. Dai, H.Y. Yu, and Q. Li, "A semisupervised feature selection with support vector machine", Journal of Applied Mathematics, Vol.2013, No.1, pp.1-11, 2013.

Z.Q. Zeng, X.D. Wang, J. Zhang, et al., "Semi-supervised feature selection based on local discriminative information", Neurocomputing, Vol.173, pp.102-109, 2016.

X.J. Zhu, "Semi-supervised learning literature survey", Computer Science, Vol.37, No.1, pp.63-77, 2008.

F.P. Nie, D. Xu, I. W. H. Tsang, et al., "Flexible manifold embedding:A framework for semi-supervised and unsupervised dimension reduction", IEEE Transactions on Image Processing, Vol.19, No.7, pp.1921-1932, 2010.

Z.G. Ma, F.P. Nie, Y. Yang, et al., "Discriminating joint feature analysis for multimedia data understanding", IEEE Transactions on Multimedia, Vol.14, No.6, pp.1662-1672, 2012.

C.J. Shi, Q.Q. Ruan and G.Y. An, "Sparse feature selection based on graph laplacian for web image annotation", Image and Vision Computing, Vol.32, No.3, pp.189-201, 2014.

F.P. Nie, H. Wang, H. Huang, et al., "Unsupervised and semisupervised learning via L1-norm graph", Proc. of The IEEE International Conference on Computer Vision, Barcelona, Spain, pp.2268-2273, 2011.

H. Xu, C. Caramanis and S. Mannor, "Sparse algorithms are not stable:A no-free-lunch theorem", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.34, No.1, pp.187-193, 2012.

K. Lounici, M. Pontil, A.B. Tsybakov, et al., "Taking advantage of sparsity in multi-task learning", arXiv preprint, arXiv:0903.1468, 2009.

F.P. Nie, H. Huang, X. Cai, et al., "Efficient and robust feature selection via joint ℓ_2,1-norms minimization", Proc. of the Advances in Neural Information Processing Systems, Vancouver, B.C., Canada, pp.1813-1821, 2010.

R. Chartrand and W. Yin, "Iteratively reweighted algorithms for compressive sensing", Proc. of The IEEE International Conference on Acoustics, Speech and Signal Processing, Caesars Palace, Las Vegas, Nevada, USA, pp.3869-3872, 2008.

F. Wang and C.S. Zhang, "Label propagation through linear neighborhoods", IEEE Transactions on Knowledge and Data Engineering, Vol.20, No.1, pp.55-67, 2007.

S. A. Nene, S. K. Nayar and H. Murase. "Columbia object image library (COIL-20)[Online] ", available:http://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php, 1996.

A.S. Georghiades, P.N. Belhumeur and D.J. Kriegman, "From few to many:Illumination cone models for face recognition under variable lighting and pose", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.23, No.6, pp.643-660, 2001.

T. Sim, S. Baker and M. Bsat, "The CMU Pose, Illumination, and Expression (PIE) database", Proc. of Fifth IEEE International Conference on Automatic Face Gesture Recognition, Washington, DC, USA, pp. 53-58, 2002.

F. Burkhardt, A. Paeschke, M. Rolfes, et al., "A database of german emotional speech", Proc. of The Ninth European Conference on Speech Communication and Technology, Lisboa, Portugal, pp.1517-1520, 2005.

O. Martin, I. Kotsia, B. Macq, et al., "The eNTERFACE' 05 audio-visual emotion database", Proc. of The 22nd International Conference on Data Engineering Workshops, Atlanta, GA, USA, pp.8-8, 2006.

ChineseLDC, "CASIA-Chinese emotional speech corpus", http://www.chineseldc.org/, 2005.

B. Schuller, S. Steidl, A. Batliner, et al., "The Interspeech 2010 paralinguistic challenge", Proc. of The INTERSPEECH, Makuhari, Japan, pp.2794-2797, 2010.

A. Hassan, R. Damper, and M. Niranjan, "On acoustic emotion recognition:Compensating for covariate shift", IEEE Transactions on Audio Speech and Language Processing, Vol.21, No.7, pp.1458-1468, 2013.

J.D. Zhao, K. Lu and X.F. He, "Locality sensitive semisupervised feature selection", Neurocomputing, Vol.71, No.10, pp.1842-1849, 2008.

Relative Articles

Supplements(0)

Cited By

Proportional views