[1]WEN Xiaohong,LIU Huaping,YAN Gaowei,et al.Nonlinear canonical correlation analysis and application based on extreme learning machine[J].CAAI Transactions on Intelligent Systems,2018,13(4):633-639.[doi:10.11992/tis.201703034]
Copy

Nonlinear canonical correlation analysis and application based on extreme learning machine

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 4 Page number: 633-639 Column: 学术论文—人工智能基础 Public date: 2018-07-05
Title:
Nonlinear canonical correlation analysis and application based on extreme learning machine
Author(s):
WEN Xiaohong1;  LIU Huaping2; 3;  YAN Gaowei1;  SUN Fuchun2; 3
1. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030600, China;
2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
3. State Key Laboratory of Intelligent Technology and Systems, Beijing 100084, China
Keywords:
canonical correlation analysis; extreme learning machine; feature extraction; multi-modal fusion; robotic grasping; object recognition; RGB-D data; neural networks
CLC:
TP391.4
DOI:
10.11992/tis.201703034
Abstract:
Canonical correlation analysis (CCA) is a statistical technique commonly used to determine the correlativity of two variables. It is difficult to accurately identify the complex underlying relationship between variables using linear CCA, so we propose a nonlinear CCA based on an extreme learning machine (ELM) for multi-modal feature extraction. First, to obtain abstract-depth feature representation, we use the ELM to perform unsupervised feature learning for each modality. Then, we use CCA to maximize the correlation between the nonlinear representations, thereby simultaneously obtaining two groups of related variables, and realize complex nonlinear and high-correlation representations of multi-modality data. Lastly, we conducted an experiment using the Cornell grasping dataset. The results show that, in comparison with other related algorithms, the proposed method significantly increases the training speed.
References:
[1] HOTELLING H. Relations between two sets of variates[J]. Biometrika, 1936, 28(3/4):321-377.
[2] SARGIN M E, YEMEZ Y, ERZIN E, et al. Audiovisual synchronization and fusion using canonical correlation analysis[J]. IEEE transactions on multimedia, 2007, 9(7):1396-1403.
[3] 侯书东, 孙权森. 稀疏保持典型相关分析及在特征融合中的应用[J]. 自动化学报, 2012, 38(4):659-665. HOU Shudong, SUN Quansen. Sparsity preserving canonical correlation analysis with application in feature fusion[J]. Acta automatica sinica, 2012, 38(4):659-665.
[4] YUAN Yunhao, SUN Quansen, GE Hongwei. Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition[J]. Pattern recognition, 2014, 47(3):1411-1424.
[5] XING Xianglei, WANG Kejun, YAN Tao, et al. Complete canonical correlation analysis with application to multi-view gait recognition[J]. Pattern recognition, 2016, 50:107-117.
[6] RASIWASIA N, PEREIRA J C, COVIELLO E, et al. A new approach to cross-modal multimedia retrieval[C]//Proceedings of the 18th ACM International Conference on Multimedia. New York, NY, USA, 2010:251-260.
[7] HARDOON D R, SZEDMAK S, SHAWE-TAYLOR J. Canonical correlation analysis:an overview with application to learning methods[J]. Neural computation, 2004, 16(12):2639-2664.
[8] ANDREW G, ARORA R, BILMES J, et al. Deep canonical correlation analysis[C]//Proceedings of the 30th International Conference on Machine Learning. Atlanta, Georgia, USA, 2013:Ⅲ-1247-Ⅲ-1255.
[9] WANG Weiran, ARORA R, LIVESCU K, et al. On deep multi-view representation learning[C]//Proceedings of the 32nd International Conference on Machine Learning. Lille, France, 2015:1083-1092.
[10] HUANG Guang, ZHU Qinyu, SIEW C K. Extreme learning machine:theory and applications[J]. Neurocomputing, 2006, 70(1/2/3):489-501.
[11] CAMBRIA E, HUANG Guangbin, KASUN L L C, et al. Extreme learning machines:trends and controversies[J]. IEEE intelligent systems, 2013, 28(6):30-59. (请核对标题信息)
[12] YU Wenchao, ZHUANG Fuzhen, HE Qing, et al. Learning deep representations via extreme learning machines[J]. Neurocomputing, 2015, 149:308-315.
[13] SUN Kai, ZHANG Jiangshe, ZHANG Chunxia, et al. Generalized extreme learning machine autoencoder and a new deep neural network[J]. Neurocomputing, 2017, 230:374-381.
[14] UZAIR M, SHAFAIT F, GHANEM B, et al. Representation learning with deep extreme learning machines for efficient image set classification[M]//MACINTYRE J. Neural Computing and Applications. London:Springer, 2015:1-13.
[15] WEI Jie, LIU Huaping, YAN Gaowei, et al. Robotic grasping recognition using multi-modal deep extreme learning machine[J]. Multidimensional systems and signal processing, 2017, 28(3):817-833.
[16] WANG Weiran, ARORA R, LIVESCU K, et al. Unsupervised learning of acoustic features via deep canonical correlation analysis[C]//Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing. Brisbane, Australia, 2015:4590-4594.
[17] LENZ I, LEE H, SAXENA A. Deep learning for detecting robotic grasps[J]. The international journal of robotics research, 2015, 34(4/5):705-724.
[18] JIANG Yun, MOSESON S, SAXENA A. Efficient grasping from RGBD images:learning using a new rectangle representation[C]//Proceedings of 2011 IEEE International Conference on Robotics and Automation. Shanghai, China, 2011:3304-3311.
[19] 韩峥, 刘华平, 黄文炳, 等. 基于Kinect的机械臂目标抓取[J]. 智能系统学报, 2013, 8(2):149-155. HAN Zheng, LIU Huaping, HUANG Wenbing, et al. Kinect-based object grasping by manipulator[J]. CAAI transactions on intelligent systems, 2013, 8(2):149-155.
[20] 温有福, 贾彩燕, 陈智能. 一种多模态融合的网络视频相关性度量方法[J]. 智能系统学报, 2016, 11(3):359-365. WEN Youfu, JIA Caiyan, CHEN Zhineng. A multi-modal fusion approach for measuring web video relatedness[J]. CAAI transactions on intelligent systems, 2016, 11(3):359-365.
[21] 姬晓飞, 秦宁丽, 刘洋. 多特征的光学遥感图像多目标识别算法[J]. 智能系统学报, 2016, 11(5):655-662. JI Xiaofei, QIN Ningli, LIU Yang. Research on multi-feature based multi-target recognition algorithm for optical remote sensing image[J]. CAAI transactions on intelligent systems, 2016, 11(5):655-662.
[22] CAMPLANI M, PAIEMENT A, MIRMEHDI M, et al. Multiple human tracking in RGB-D data:a survey[J]. arXiv:1606.04450, 2016.
Similar References:

Memo

-

Last Update: 2018-08-25

Copyright © CAAI Transactions on Intelligent Systems