【摘要】：近來(lái),稀疏表示分類算法已經(jīng)在模式識(shí)別和特征提取領(lǐng)域獲得了廣泛的關(guān)注.受最近提出的稀疏表示判別投影算法啟發(fā),本文提出了一種新的特征加權(quán)組稀疏判別投影算法(Feature weighted group sparse classification steered discriminative projection,FWGSDP).首先,提出特征加權(quán)組稀疏分類算法(Feature weighted group sparsebased classification,FWGSC)進(jìn)行稀疏系數(shù)編碼,該算法采用帶特征加權(quán)約束的保局性信息,能夠魯棒地重構(gòu)給定的輸入數(shù)據(jù);其次,通過(guò)類內(nèi)重構(gòu)散度最小、類間重構(gòu)散度最大為目標(biāo)計(jì)算最優(yōu)投影判別矩陣,使得輸入數(shù)據(jù)具有最佳的模式分類效果;最后,提出迭代重約束稀疏編碼方法并結(jié)合特征分解操作進(jìn)行FWGSDP模型高效求解.在Ex Yale B,PIE和AR三個(gè)人臉數(shù)據(jù)庫(kù)的實(shí)驗(yàn)驗(yàn)證了所提算法在普通數(shù)據(jù)和帶噪數(shù)據(jù)中的分類效果都優(yōu)于現(xiàn)存的算法.
[Abstract]:Recently, sparse representation classification algorithm has received extensive attention in the field of pattern recognition and feature extraction. Inspired by the recently proposed sparse representation discriminant projection algorithm, a new feature weighted group sparse discriminant projection algorithm (Feature weighted group sparse classification steered discriminative projection,FWGSDP) is proposed in this paper. Firstly, a feature weighted group sparse classification algorithm (Feature weighted group sparsebased classification,FWGSC) is proposed for sparse coefficient coding. The algorithm adopts the local information with feature weighted constraints and can robust reconstruct the given input data. Secondly, the optimal projection discriminant matrix is calculated by the minimum intra-class reconstruction divergence and the maximum inter-class reconstruction divergence, so that the input data has the best pattern classification effect. Finally, an iterative reconstrained sparse coding method is proposed and combined with feature decomposition operation to solve the FWGSDP model efficiently. The experiments of pie and AR face databases in Ex Yale B verify that the proposed algorithm is superior to the existing algorithms in both ordinary data and noisy data.
【作者單位】： 浙江工業(yè)大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;衢州職業(yè)技術(shù)學(xué)院信息工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(61502424;61379123) 浙江省自然科學(xué)基金(LY15E050007;LY15F030014;LQ14F030003)資助~~
【分類號(hào)】：TP311.13

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