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基于譜回歸特征降維與后向傳播神經(jīng)網(wǎng)絡(luò)的識別方法研究

鄔戰(zhàn)軍,  牛敏,  許冰,  牛燕雄,  耿天琪,  張帆,  滿達(dá)

鄔戰(zhàn)軍, 牛敏, 許冰, 牛燕雄, 耿天琪, 張帆, 滿達(dá). 基于譜回歸特征降維與后向傳播神經(jīng)網(wǎng)絡(luò)的識別方法研究[J]. 電子與信息學(xué)報(bào), 2016, 38(4): 978-984. doi: 10.11999/JEIT150781
引用本文: 鄔戰(zhàn)軍, 牛敏, 許冰, 牛燕雄, 耿天琪, 張帆, 滿達(dá). 基于譜回歸特征降維與后向傳播神經(jīng)網(wǎng)絡(luò)的識別方法研究[J]. 電子與信息學(xué)報(bào), 2016, 38(4): 978-984. doi: 10.11999/JEIT150781
WU Zhanjun, NIU Min, XU Bing, NIU Yanxiong, GENG Tianqi, ZHANG Fan, MAN Da . Research on Recognition Method Based on Spectral Regression and Back Propagation Neural Network[J]. Journal of Electronics & Information Technology, 2016, 38(4): 978-984. doi: 10.11999/JEIT150781
Citation: WU Zhanjun, NIU Min, XU Bing, NIU Yanxiong, GENG Tianqi, ZHANG Fan, MAN Da . Research on Recognition Method Based on Spectral Regression and Back Propagation Neural Network[J]. Journal of Electronics & Information Technology, 2016, 38(4): 978-984. doi: 10.11999/JEIT150781

基于譜回歸特征降維與后向傳播神經(jīng)網(wǎng)絡(luò)的識別方法研究

doi: 10.11999/JEIT150781 cstr: 32379.14.JEIT150781

Research on Recognition Method Based on Spectral Regression and Back Propagation Neural Network

  • 摘要: 采用后向傳播(BP)神經(jīng)網(wǎng)絡(luò)對空間目標(biāo)進(jìn)行識別時(shí)，高維的輸入特征導(dǎo)致網(wǎng)絡(luò)結(jié)構(gòu)復(fù)雜，識別性能降低。針對上述難點(diǎn)，該文提出一種基于譜回歸(SR)特征降維與BP神經(jīng)網(wǎng)絡(luò)的識別方法。該方法首先對空間目標(biāo)進(jìn)行HOG特征提取，然后將提取的高維HOG特征進(jìn)行SR降維，最后把降維后的數(shù)據(jù)通過BP分類器進(jìn)行訓(xùn)練識別。實(shí)驗(yàn)結(jié)果表明：該方法的降維和識別特性優(yōu)于傳統(tǒng)降維方法PCA, KPAC, LPP, KLPP等，能夠兼顧實(shí)時(shí)性和準(zhǔn)確性，提高了識別性能。
    Abstract: When using Back Propagation (BP) neural network to recognize the spatial target, the high dimensional input features induce the complexity of the network structure and the poor performance of the recognition. In this paper, a new recognition method based on Spectral Regression (SR) feature dimension reduction and BP neural network is proposed for the above difficulties. Firstly, the HOG features are extracted from the spatial object, and then the feature dimensions are reduced by SR. Finally, the BP classifier is used to train the data. Experimental results show that the proposed method is better than the traditional dimension reduction methods such as PCA, KPCA, LPP, KLPP in dimension reduction and recognition, which can juggle real-time and accuracy, thus improving the recognition performance.
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出版歷程
  • 收稿日期:  2015-06-29
  • 修回日期:  2015-11-09
  • 刊出日期:  2016-04-19

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