Feature selection via uncorrelated discriminant sparse regression for multimedia analysis

Multimedia Tools and Applications - Tập 82 - Trang 619-647 - 2022
Shuangle Guo1, Jianguang Zhang2,3, Wenting Zhang4, Zhifei Song5, Chunmei Meng6
1The School of Information Engineering, Binzhou University, Binzhou, China
2The College of Mathematics and Computer Science, Hengshui University, Hengshui, China
3The School of Computer Science & Software Engineering, Shenzhen University, Shenzhen, China
4The College Electronics & Information Engineering, Hengshui University, Hengshui, China
5The Office of Academic Research, Hengshui University, Hengshui, China
6The College of Continuing Education, Hengshui University, Hengshui, China

Tóm tắt

As an important part of multimedia analysis applications, feature selection has attracted much attention during the past decades. Lots of feature selection methods have been proposed, but most of them neglect to consider the correlation between the selected features, which leads to the feature redundancy problem. In this paper, we propose a novel supervised feature selection method, termed as Uncorrelated Discriminant Sparse Regression (UDSR). This method is an organic combination of discriminant sparse regression and uncorrelated constraint. In this method, the discriminant sparse regression ensures the discriminant power of the selected features, and the uncorrelated constraint avoids the redundancy of selected features. Thus the features selected by our method are not only discriminative but also uncorrelated with each other. The method can be applied to a wide range of multimedia applications. Experiments are conducted on two video datasets and four image datasets. The experimental results show that the proposed method has better performance for multimedia analysis, compared to the baseline and six state-of-the-art relative methods.

Tài liệu tham khảo

Chao YW, Wang Z, He Y, Wang J, Deng J (2015) Hico: a benchmark for recognizing human-object interactions in images. In: 2015 IEEE international conference on computer vision (ICCV), pp 1017–1025. IEEE Chen X, Yuan G, Nie F, Zhong M (2018) Semi-supervised feature selection via sparse rescaled linear square regression. IEEE Trans Knowl & Data Eng 32(1):165–176 Delaitre V, Laptev I, Sivic J (2010) Recognizing human actions in still images: a study of bag-of-features and part-based representations. In: BMVC, vol 2, p 7 Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3(02):185–205 Duda RO, Hart PE, Stork DG (2001) Pattern classification 2nd edn. Wiley, New York Golub GH, Loan V (1996) Matrix computations. Math Gaz, vol 74(469) Gu K, Liu H, Xia Z, Qiao J, Lin W, Thalmann D (2021) Pm2.5 monitoring: use information abundance measurement and wide and deep learning. IEEE Trans Neural Netw Learn Syst 32(10):4278–4290 Gu K, Xia Z, Qiao J (2019) Stacked selective ensemble for pm 2.5 forecast. IEEE Trans Instrum Meas 69(3):660–671 Gu K, Xia Z, Qiao J, Lin W (2019) Deep dual-channel neural network for image-based smoke detection. IEEE Trans Multimedia 22(2):311–323 Gu K, Zhai G, Lin W, Liu M (2015) The analysis of image contrast: from quality assessment to automatic enhancement. IEEE Trans Cybern 46(1):284–297 Gu K, Zhang Y, Qiao J (2020) Ensemble meta-learning for few-shot soot density recognition. IEEE Trans Industr Inform 17(3):2261–2270 Gupta A, Kembhavi A, Davis LS (2009) Observing human-object interactions: using spatial and functional compatibility for recognition. IEEE Trans Pattern Anal Mach Intell 31(10):1775–1789 Han D, Kim J (2015) Unsupervised simultaneous orthogonal basis clustering feature selection. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 5016–5023. IEEE Han Y, Yang Y, Zhou X (2013) Co-regularized ensemble for feature selection. In: Twenty-third international joint conference on artificial intelligence, pp 1380–1386 Han Y, Zhang J, Xu Z, Yu S-I (2013) Discriminative multi-task feature selection. In: Workshops at the twenty-seventh AAAI conference on artificial intelligence He X, Cai D, Niyogi P (2006) Laplacian score for feature selection. In: Advances in neural information processing systems, pp 507–514 Henry ER, Hofrichter J (1992) [8] Singular value decomposition: application to analysis of experimental data. Meth Enzymol 210:129–192 Hou C, Nie F, Li X, Yi D, Wu Y (2013) Joint embedding learning and sparse regression: a framework for unsupervised feature selection. IEEE Trans Cybern 44(6):793–804 Huang Q, Tao D, Li X, Jin L, Wei G (2011) Exploiting local coherent patterns for unsupervised feature ranking. IEEE Trans Syst Man Cybern, Part B (Cybern) 41(6):1471–1482 Ikizler N, Cinbis RG , Pehlivan S, Duygulu P (2008) Recognizing actions from still images. In: 19th International conference on pattern recognition, 2008. ICPR, 2008, pp 1–4. IEEE Jégou H, Douze M, Schmid C, Pérez P (2010) Aggregating local descriptors into a compact image representation. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR), pp 3304–3311. IEEE Kumar J, Peng Y, Doermann D (2014) Structural similarity for document image classification and retrieval. Pattern Recogn Lett 43(1):119–126 Kwak S, Cho M, Laptev I (2016) Thin-slicing for pose: learning to understand pose without explicit pose estimation. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 4938–4947. IEEE Li Y, Xia R, Huang Q, Xie W, Li X (2017) Survey of spatio-temporal interest point detection algorithms in video. IEEE Access 5:10323–10331 Li X, Zhang H, Zhang R, Liu Y, Nie F (2018) Generalized uncorrelated regression with adaptive graph for unsupervised feature selection. IEEE Trans Neural Netw Learn Syst 30(5):1587–1595 Li X, Zhang H, Zhang R, Nie F (2019) Discriminative and uncorrelated feature selection with constrained spectral analysis in unsupervised learning. IEEE Trans Image Process 29(1):2139–2149 Liao X, Li K, Zhu X, Liu KR (2020) Robust detection of image operator chain with two-stream convolutional neural network. IEEE IEEE J Sel Top Signal Process 14(5):955–968 Liao Xin, Yin Jiaojiao, Chen Mingliang, Qin Zheng (2020) Adaptive payload distribution in multiple images steganography based on image texture features. IEEE Trans Dependable Secure Comput Liao X, Yu Y, Li B, Li Z, Qin Z (2019) A new payload partition strategy in color image steganography. IEEE Trans Circuits Syst Video Technol 30 (3):685–696 Liu H, Sun J, Liu L, Zhang H (2009) Feature selection with dynamic mutual information. Pattern Recogn 42(7):1330–1339 Liu J, Yang Y, Shah M (2009) Learning semantic visual vocabularies using diffusion distance. In: 2009 IEEE computer society conference on computer vision and pattern recognition (CVPR 2009), Miami, Florida, USA, pp 461–468. IEEE Liu K, Yang X, Yu H, Mi J, Wang P, Chen X (2019) Rough set based semi-supervised feature selection via ensemble selector. Knowl-Based Syst 165:282–296 Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110 Mohino-Herranz I, Gil-Pita R, garcía-gómez J, Rosa-Zurera M, Seoane F (2020) A wrapper feature selection algorithm: an emotional assessment using physiological recordings from wearable sensors. Sensors 20(1):309 Nie F, Huang H, Xiao C, Chris H, Ding Q (2010) Efficient and robust feature selection via joint l2, 1-norms minimization. In: Advances in neural information processing systems (NIPS), Vancouver, British Columbia, Canada, pp 1813–1821 Nie F, Xiang S, Jia Y, Zhang C, Yan S (2008) Trace ratio criterion for feature selection. In: AAAI, vol 2, pp 671–676 Nie F, Yang S, Zhang R, Li X (2018) A general framework for auto-weighted feature selection via global redundancy minimization. IEEE Trans Image Process 28(5):2428–2438 Pang Y, Zhou B, Feiping N (2019) Simultaneously learning neighborship and projection matrix for supervised dimensionality reduction. IEEE Trans Neural Netw Learn Syst 30(9):2779–2793 Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238 Prest A, Schmid C, Ferrari V (2012) Weakly supervised learning of interactions between humans and objects. IEEE Trans Pattern Anal Mach Intell 34 (3):601–614 Ramjee S, Gamal AE (2019) Sáez JA, Corchado E (2019) Ksufs: a novel unsupervised feature selection method based on statistical tests for standard and big data problems. IEEE Access 7:99754–99770 Sayed GI, Hassanien AE, Azar AT (2019) Feature selection via a novel chaotic crow search algorithm. Neural Comput Appl 31(1):171–188 Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local svm approach. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, pp 32–36 Sharma A, Paliwal KK, Imoto S, Miyano S (2014) A feature selection method using improved regularized linear discriminant analysis. Mach Vis Appl 25(3):775–786 Shi C, Gu Z, Duan C, Tian Q (2020) Multi-view adaptive semi-supervised feature selection with the self-paced learning. Signal Process 168:107332 Shirzad MB, Keyvanpour MR (2015) A feature selection method based on minimum redundancy maximum relevance for learning to rank. In: AI & Robotics (IRANOPEN), pp 1–5. IEEE Song F, Mei D, Li H (2010) Feature selection based on linear discriminant analysis. In: International conference on intelligent system design and engineering application, vol 1, pp 746–749. IEEE, 2010 Tang J, Hu X, Gao H, Liu H (2014) Discriminant analysis for unsupervised feature selection. In: proceedings of the SIAM international conference on data mining, pp 938–946, SIAM, 2014 Wang H, Kläser A, Schmid C, Liu C-L (2013) Dense trajectories and motion boundary descriptors for action recognition. Int J Comput Vis 103 (1):60–79 Wang D, Nie F, Huang H (2014) Unsupervised feature selection via unified trace ratio formulation and k-means clustering (track). In: Joint european conference on machine learning and knowledge discovery in databases. Springer, pp 306–321 Wang D, Nie F, Huang H (2015) Feature selection via global redundancy minimization. IEEE Trans Knowl Data Eng 27(10):2743–2755 Wang H, Ullah MM , Klaser A, Laptev I, Schmid C et al (2009) Evaluation of local spatio-temporal features for action recognition. In: BMVC 2009-British machine vision conference Wang J, Wu L, Kong J, Li Y, Zhang B (2013) Maximum weight and minimum redundancy: a novel framework for feature subset selection. Pattern Recogn 46(6):1616–1627 Xiao C, Nie F, Huang H, Chris H, Ding Q (2011) Multi-class l2,1-norm support vector machine. In: 11th IEEE international conference on data mining, ICDM, 2011, Vancouver, BC Canada, 2011, pp 91–100. IEEE Yang Y, Li H, Lin X, Ming D (2010) Recursive feature selection based on minimum redundancy maximum relevancy. In: 2010 3rd International symposium on parallel architectures, algorithms and programming, pp 281–285. IEEE Yang Y, Shen HT, Ma Z, Huang Z, Zhou X (2011) L2, 1-norm regularized discriminative feature selection for unsupervised. In: Twenty-second international joint conference on artificial intelligence Yang Z, Wang H, Han Y, Zhu X (2018) Discriminative multi-task multi-view feature selection and fusion for multimedia analysis. Multimed Tools Appl 77(3):3431–3453 Yao B, Fei-Fei L (2010) Grouplet: a structured image representation for recognizing human and object interactions. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR), pp 9–16. IEEE Zhang C, Hu Q, Fu H, Zhu P, Cao X (2017) Latent multi-view subspace clustering. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 4279–4287. IEEE Zheng W, Zhu X, Wen G, Zhu Y, Yu H , Gan J (2018) Unsupervised feature selection by self-paced learning regularization. Pattern Recognit Lett