Journal of Shandong University(Engineering Science) ›› 2023, Vol. 53 ›› Issue (6): 26-34.doi: 10.6040/j.issn.1672-3961.0.2022.284

• Machine Learning & Data Mining • Previous Articles    

Log-based robust kernel ridge regression for subspace clustering

ZHANG Xin, FEI Keke   

  1. College of Computer Science &
    Technology, Qingdao University, Qingdao 266071, Shandong, China
  • Published:2023-12-19

PDF (PC)

161

CLC Number: 

  • TP181
[1] CHENG B, YANG J, YAN S, et al. Learning with L1-graph for image analysis[J]. IEEE Transactions on Image Process, 2010, 19(4): 858-866.
[2] KOMODAKIS N. Image completion using global optimization[C] //Proceedings of Computer Vision and Pattern Recognition. New York, USA: IEEE, 2006: 442-452.
[3] JI H, LIU C, SHEN Z, et al. Robust video denoising using low rank matrix completion[C] //Proceedings of Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010: 13-18.
[4] VIDAL R, MA Y, SASTRY S. Generalized principal component analysis(GPCA)[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(12): 1945-1959.
[5] GRUBER A, WEISS Y. Multibody factorization with uncertainty and missing data using the EM algorithm[C] //Proceedings of Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2004: 707-714.
[6] HO J, YANG M H, LIM J, et al. Clustering appearances of objects under varying illumination conditions[C] //Proceedings of Computer Vision and Pattern Recognition. Madison, USA: IEEE, 2003: 11-18.
[7] SUI Y, WANG G, ZHANG L. Sparse subspace clustering via low-rank structure propagation[J]. Pattern Recognition, 2019, 95: 261-271.
[8] DENG C, HE X, WU X, et al. Non-negative matrix factorization on manifold[C] //Proceedings of Eighth IEEE International Conference on Data Mining. Pisa, Italy: IEEE, 2008: 63-72.
[9] ELHAMIFAR E, VIDAL R. Sparse subspace clustering: algorithm, theory, and applications[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(11): 2765-2781.
[10] LIU G, LIN Z, YAN S, et al. Robust recovery of subspace structures by low-rank representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 35(1): 171-184.
[11] WANG Y, ZHENG Y, WANG Z, et al. Time-weighted kernel-sparse-representation-based real-time nonlinear multimode process monitoring[J]. IEEE Transactions on Industrial Informatics, 2021, 18(4): 2411-2421.
[12] CAI B, LU G F. Tensor subspace clustering using consensus tensor low-rank representation[J]. Information Sciences, 2022, 609: 46-59.
[13] LIU M, WANG Y, SUN J, et al. Adaptive low-rank kernel block diagonal representation subspace clustering[J]. Applied Intelligence, 2022, 52(2): 2301-2316.
[14] ZHANG G Y, CHEN X W, ZHOU Y R, et al. Kernelized multi-view subspace clustering via auto-weighted graph learning[J]. Applied Intelligence, 2022, 52(1): 716-731.
[15] ABHADIOMHEN S E, WANG Z Y, SHEN X J. Coupled low rank representation and subspace clustering[J]. Applied Intelligence, 2022, 52(1): 530-546.
[16] WANG S, CHEN Y, CEN Y, et al. Nonconvex low-rank and sparse tensor representation for multi-view subspace clustering[J]. Applied Intelligence, 2022, 52(13): 14651-14664.
[17] WEI L, ZHANG F, CHEN Z, et al. Subspace clustering via adaptive least square regression with smooth affinities[J]. Knowledge-Based Systems, 2022, 239: 107950.
[18] LU C Y, MIN H, ZHAO Z Q, et al. Robust and efficient subspace segmentation via least squares regression[C] //Proceedings of European Conference on Computer Vision. Florence, Italy: Springer, 2012: 347-360.
[19] PENG C, CHENG Q. Discriminative ridge machine: a classifier for high-dimensional data or imbalanced data[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 32(6): 2595-2609.
[20] YU Y F, XU G, JIANG M, et al. Joint transformation learning via the L2,1-norm metric for robust graph matching[J]. IEEE Transactions on Cybernetics, 2019, 51(2): 521-533.
[21] MA Y, LI C, MEI X, et al. Robust sparse hyperspectral unmixing with 2,1 norm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 55(3): 1227-1239.
[22] PENG C, ZHANG Y, CHEN Y, et al. Log-based sparse nonnegative matrix factorization for data representation[J]. Knowledge-Based Systems, 2022, 251(6): 109127.
[23] PENG C, LIU Y, KANG K, et al. Hyperspectral image denoising using non-convex local low-rank and sparse separation with spatial-spectral total variation regularization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-17.
[24] LIU Y, ZHANG Q, CHEN Y, et al. Hyperspectral image denoising with log-based robust PCA[C] //Proceedings of International Conference on Image Processing. Anchorage, USA: IEEE, 2021: 1634-1638.
[25] PENG C, ZHANG Z, KANG Z, et al. Nonnegative matrix factorization with local similarity learning[J]. Information Sciences, 2021, 562: 325-346.
[26] CHEN Y, GUO Y, WANG Y, et al. Denoising of hyperspectral images using nonconvex low rank matrix approximation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(9): 5366-5380.
[27] TON K C, YUN S. An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems[J]. Pacific Journal of Optimization, 2010, 6(3): 615-640.
[28] ZHOU T, YANG J, LIANG L. Enhanced sparse subspace clustering by manifold regularization for hyperspectral image[C] //Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science. New York, USA: ACM, 2019: 450-453.
[29] KANG Z, LU X, LU Y, et al. Structure learning with similarity preserving[J]. Neural Networks, 2020, 129(8): 138-148.
[30] KANG Z, PENG C, CHENG Q, et al. Structured graph learning for clustering and semi-supervised classification[J]. Pattern Recognition, 2021, 110: 107627.
[31] PENG C, KANG Z, LI H, et al. Subspace clustering using log-determinant rank approximation[C] //Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2015: 925-934.
[32] CANDES E J, LI X, MA Y, et al. Robust principal component analysis?[J]. Journal of the ACM(JACM), 2011, 58(3): 1-37.
[33] LEE S, PARK Y T, et al. A novel feature selection method based on normalized mutual information[J]. Applied Intelligence, 2012, 37(1): 100-120.
[34] KANG Z, PENG C, CHENG Q. Top-n recommender system via matrix completion[C] //Proceedings of the AAAI Conference on Artificial Intelligence. Phoenix, USA: AAAI, 2016: 179-184.
[35] PENG C, CHEN Y, KANG Z, et al. Robust principal component analysis: a factorization-based approach with linear complexity[J]. Information Sciences, 2019, 513: 581-599.
[36] ZHOU P, LU C, FENG J, et al. Tensor low-rank representation for data recovery and clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 43(5): 1718-1732.
[37] LIU J, CHEN Y, ZHANG J, et al. Enhancing low-rank subspace clustering by manifold regularization[J]. IEEE Transactions on Image Processing, 2014, 23(9): 4022-4030.
[38] LI C G, VIDAL R. Structured sparse subspace clustering: a unified optimization framework[C] //Proceedings of Computer Vision and Pattern Recognition. Boston, USA: IEEE, 2015: 277-286.
[39] PATEL V M, VIDAL R. Kernel sparse subspace clustering[C] //Proceedings of IEEE International Conference on Image Processing. Paris, France: IEEE, 2014: 2849-2853.
[40] KANG Z, LU Y, SU Y, et al. Similarity learning via kernel preserving embedding[C] //Proceedings of the AAAI Conference on Artificial Intelligence. Hawaii, USA: AAAI, 2019: 4057-4064.
[41] PENG C, CHENG J, CHENG Q. A supervised learning model for high-dimensional and large-scale data[J]. ACM Transactions on Intelligent Systems and Technology(TIST), 2016, 8(2): 1-23.
[1] Yan PENG,Tingting FENG,Jie WANG. An integrated learning approach for O3 mass concentration prediction model [J]. Journal of Shandong University(Engineering Science), 2020, 50(4): 1-7.
[2] Yibin WANG,Tianli LI,Yusheng CHENG,Kun QIAN. Label distribution learning based on kernel extreme learning machine auto-encoder [J]. Journal of Shandong University(Engineering Science), 2020, 50(3): 58-65.
[3] Chunyang LI,Nan LI,Tao FENG,Zhuhe WANG,Jingkai MA. Abnormal sound detection of washing machines based on deep learning [J]. Journal of Shandong University(Engineering Science), 2020, 50(2): 108-117.
[4] Yingda LI,Zongxia XIE. Support vector regression algorithm based on kernel similarity reduced strategy [J]. Journal of Shandong University(Engineering Science), 2019, 49(3): 8-14.
[5] Kuo PANG,Siqi CHEN,Xiaoying SONG,Li ZOU. Linguistic concept formal decision context analysis based on granular computing [J]. Journal of Shandong University(Engineering Science), 2018, 48(6): 74-81.
[6] HE Zhengyi, ZENG Xianhua, GUO Jiang. An ensemble method with convolutional neural network and deep belief network for gait recognition and simulation [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 88-95.
[7] WANG Tingting, ZHAI Junhai, ZHANG Mingyang, HAO Pu. K-NN algorithm for big data based on HBase and SimHash [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 54-59.
[8] CUI Xiaosong, WANG Ying, MENG Jia, ZOU Li. Online business self-evaluation system based on linguistic-valued similarity reasoning [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(1): 1-7.
[9] YAO Yu, FENG Jian, ZHANG Huaguang, HAN Kezhen. Weighted hyper-ellipsoidal support vector data description with negative samples for outlier detection [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 195-202.
[10] LI Sushu, WANG Shitong, LI Tao. A feature selection method based on LS-SVM and fuzzy supplementary criterion [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(3): 34-42.
[11] LIU Yingxia, WANG Xichang, TANG Xiaoli, CHANG Faliang. Object detection algorithm based on Bayesian probability estimation in wavelet domain [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(2): 63-70.
[12] WANG Mei, ZENG Zhaohu, SUN Yingqi, YANG Erlong, SONG Kaoping. Bayesian combination of SVR on regularization path based on KNN of input [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 8-14.
[13] CHEN Zehua, SHANG Xiaohui, CHAI Jing. Neighborhood related multiple-instance classifiers based on integrated Hausdorff distance [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 15-22.
[14] HE Zhengyi, ZENG Xianhua, QU Shengwei, WU Zhilong. The time series prediction model based on integrated deep learning [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 40-47.
[15] WANG Zhiqiang, WEN Yimin, LI Fang. Collaborative recommendation for scenic spots based on multi-aspect ratings [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 54-61.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd