Robot Perceptual Learning Method Based on Incremental Bidirectional Principal Component Analysis

WANG Xiaofeng; ZHANG Minglu; LIU Jun

doi:10.11999/JEIT170561

Volume 40 Issue 3

Mar. 2018

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(3): 618-625

WANG Xiaofeng, ZHANG Minglu, LIU Jun. Robot Perceptual Learning Method Based on Incremental Bidirectional Principal Component Analysis[J]. Journal of Electronics & Information Technology, 2018, 40(3): 618-625. doi: 10.11999/JEIT170561

Citation:

WANG Xiaofeng, ZHANG Minglu, LIU Jun. Robot Perceptual Learning Method Based on Incremental Bidirectional Principal Component Analysis[J]. Journal of Electronics & Information Technology, 2018, 40(3): 618-625. doi: 10.11999/JEIT170561

Citation:

PDF( 4157 KB)

Robot Perceptual Learning Method Based on Incremental Bidirectional Principal Component Analysis

doi: 10.11999/JEIT170561

1.
2.
(School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China)

Funds:

The National Natural Science Foundation of China (61503119, 61473113), The Tianjin Natural Science Foundation (15JCYBJC19800, 16JCZDJC30400), The Tianjin Intelligent Manufacturing and Technology Key Project (15ZXZNGX00090)

Received Date: 2017-06-09
Rev Recd Date: 2017-10-13
Publish Date: 2018-03-19

Abstract

Abstract

Existing Candid Covariance-free Incremental PCA (CCIPCA) has the limitation of the stable image inherent covariance, and a Generalized CCIPCA (GCCIPCA) with an appended term of the mean difference vector is presented. It can be considered that the CCIPCA is only a special case of the GCCIPCA and can extend the scope of the algorithm. Then, the incremental learning of the proposed GCCIPCA is innovated to the existing Bi-Directional PCA (BDPCA), and the called Incremental BDPCA (IBDPCA) is used for the robot perceptual learning and it can be used to incrementally compute the principal components without estimating the similar scatter matrixes in the row and column directions, which can build up the real-time processing speed greatly. Finally, the blocks grasped by the robot are used as the perceptual objects, and the experimental results demonstrate that the proposed algorithm works well, and the convergence rate, the classification recognition rate, the computation time and the required memory are improved significantly.
- Robot perceptual learning,
- Incremental learning,
- Dimension reduction,
- Candid Covariance-free Incremental PCA (CCIPCA),
- Bi-Directional PCA (BDPCA)

FullText(HTML)

References(25)

References

GATSOULIS Y and MCGINNITY T M. Intrinsically motivated learning systems based on biologically-inspired novelty detection[J]. Robotics and Autonomous Systems, 2015, 68: 12-20. doi: 10.1016/j.robot.2015.02.006.

WENG J Y, MCCLELLAND J, PENTLAND A, et al. Artificial intelligence-autonomous mental development by robots and animals[J]. Science, 2001, 291(5504): 599-600. doi: 10.1126/science.291.5504.599.

JI ZP and WENG J Y. A developmental wherewhat network for concurrent and interactive visual attention and recognition[J]. Robotics and Autonomous Systems, 2015, 71: 35-48. doi: 10.1016/j.robot.2015.03.004.

SIGAUD O and DRONIOU A. Towards deep developmental learning[J]. IEEE Transactions on Cognitive and Developmental Systems, 2016, 8(2): 99-114. doi: 10.1109/ TAMD.2015.2496248.

YAN H, ANG M H and POO A N. A Survey on perception methods for human-robot interaction in social robots[J]. International Journal of Social Robotics, 2014, 6(1): 85-119. doi: 10.1007/s12369-013-0199-6.

LI Lingjun, LIU Shigang, PENG Yali, et al. Overview of principal component analysis algorithm[J]. Optik- International Journal for Light and Electron Optics, 2016, 127(9): 3935-3944. doi: 10.1016/j.ijleo.2016.01.033.

PARK G and KONNO A. Imitation learning framework based on principal component analysis[J]. Advanced Robotics, 2015, 29(9): 639-656. doi: 10.1080/01691864.2015.1007084.

WENG J Y, ZHANG Y L, and HWANG W S. Candid covariance-free incremental principal component analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(8): 1034-1040. doi: 10.1109/TPAMI. 2003.1217609.

谢自强, 葛为民, 王肖锋, 等. 发展型机器人实时特征提取方法研究[J]. 机器人, 2017, 39(2): 189-196. doi: 10.13973/ j.cnki.robot.2017.0189.

XIE Ziqiang, GE Weimin, WANG Xiaofeng, et al. Real time feature extraction method of developmental robot[J]. Robot, 2017, 39(2): 189-196. doi: 10.13973/j.cnki.robot.2017.0189.

BAI H L and CHEN M. CCIPCA-OPCSC: An online method for detecting shared congestion paths[J]. Computer Networks, 2012, 56(1): 399-411. doi: 10.1016/j.comnet.2011.09.016.

WANG J. Generalized 2-D principal component analysis by Lp-Norm for image analysis[J]. IEEE Transactions on Cybernetics, 2016, 46(3): 792-803. doi: 10.1109/TCYB.2015. 2416274.

曹明明, 干宗良, 崔子冠, 等. 基于2D-PCA特征描述的非负权重邻域嵌入人脸超分辨率重建算法[J]. 电子与信息学报, 2015, 37(4): 777-783. doi: 10.11999/JEIT140739.

CAO Mingming, GAN Zongliang, CUI Ziguan, et al. Novel neighbor embedding face hallucination based on non-negative weights and 2D-PCA feature[J]. Journal of Electronics Information Technology, 2015, 37(4): 777-783. doi: 10.11999/ JEIT140739.

YANG Wankou, SUN Changyin, and RICANEK K. Sequential row-column 2DPCA for face recognition[J]. Neural Computing and Applications, 2012, 21(7): 1729-1735. doi: 10.1007/s00521-011-0676-5.

XU F, GU G, KONG X, et al. Object tracking based on two- dimensional PCA[J]. Optical Review, 2016, 23(2): 231-243. doi: 10.1007/s10043-015-0178-2.

HUANG J, MA Y, MEI X G, et al. A hybrid spatial-spectral denoising method for infrared hyperspectral images using 2DPCA[J]. Infrared Physics Technology, 2016, 79: 68-73. doi: 10.1016/j.infrared.2016.09.009.

PEI J, HUANG Y, HUO W, et al. SAR imagery feature extraction using 2DPCA-based two-dimensional neighborhood virtual points discriminant embedding[J]. IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing, 2017, 9(6): 2206-2214. doi: 10.1109/JSTARS.2016.2555938.

ZUO Wangmeng, ZHANG David, and WANG Kuanquan. Bidirectional PCA with assembled matrix distance metric for image recognition[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B(Cybernetics), 2006, 36(4): 863-872. doi: 10.1109/TSMCB.2006.872274.

SUN Yanfeng, CHEN Shangyou, and YIN Baocai. Color face recognition based on quaternion matrix representation[J]. Pattern Recognition Letters, 2011, 32(4): 597-605. doi: 10.1016/j.patrec.2010.11.004.

YANG Wankou, SUN Changyin, ZHANG Lei, et al. Laplacian bidirectional PCA for face recognition[J]. Neurocomputing, 2010, 74(1/3): 487-493. doi: 10.1016/j. neucom.2010.08.020.

NGUYEN T H B and KIM H. Novel and efficient pedestrian detection using bidirectional PCA[J]. Pattern Recognition, 2013, 46(8): 2220-2227. doi: 10.1016/j.patcog.2013.01.007.

REN Chuanxian and DAI Daoqing. Incremental learning of bidirectional principal components for face recognition[J]. Pattern Recognition, 2010, 43(1): 318-330. doi: 10.1016/ j.patcog.2009.05.020.

余映, 王斌, 张立明. 一种面向数据学习的快速PCA算法[J]. 模式识别与人工智能, 2009, 22(4): 567-573. doi: 10.16451/ j.cnki.issn1003-6059.2009.04.003.

YU Ying, WANG Bin, and ZHANG Liming. A fast data- oriented algorithm for principal component analysis[J]. Pattern Recognition and Artificial Intelligence, 2009, 22(4): 567-573. doi: 10.16451/j.cnki.issn1003-6059.2009.04.003.

Relative Articles

Supplements(0)

Cited By

Proportional views