本文選題：變異粒子群　切入點(diǎn)：在線字典學(xué)習(xí)　出處：《計(jì)算機(jī)工程與科學(xué)》2017年09期 　論文類型：期刊論文

【摘要】：針對(duì)在線字典學(xué)習(xí)需將所有字典原子全部更新、優(yōu)化方向難以進(jìn)行估算等原因造成精度下降的不足,提出基于變異粒子群優(yōu)化的在線字典學(xué)習(xí)算法。算法基于ODL的基礎(chǔ),在字典學(xué)習(xí)的迭代過程中對(duì)梯度下降函數(shù)進(jìn)行優(yōu)化。首先選出特殊字典原子,利用各個(gè)字典原子之間關(guān)系,線性表征當(dāng)前選出的原子,以線性系數(shù)作為粒子群中的粒子位置。然后將基于變異粒子群的原子更新模式引入字典學(xué)習(xí),利用變異粒子群優(yōu)化算法進(jìn)行粒子的適應(yīng)度淘汰,選擇更適合的粒子進(jìn)行下一輪的字典更新。此外,利用中間變量將歷史參考數(shù)據(jù)引入變異粒子群模型以引導(dǎo)其優(yōu)化方向,提高字典的準(zhǔn)確性和有效性。利用高分一號(hào)遙感影像進(jìn)行實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明該算法優(yōu)于同類方法,有更好的噪音抑制效果,同時(shí)也提高了大規(guī)模的遙感圖像處理性能。
[Abstract]:In order to solve the problem that all the dictionary atoms need to be updated and the optimization direction is difficult to estimate, an online dictionary learning algorithm based on mutation particle swarm optimization (VPSO) is proposed. The algorithm is based on ODL. In the iterative process of dictionary learning, the gradient descent function is optimized. Firstly, special dictionary atoms are selected, and the current selected atoms are represented linearly by using the relationship between each dictionary atom. The linear coefficient is used as the particle position in the particle swarm, and then the atomic renewal model based on the variable particle swarm is introduced into the dictionary learning, and the particle fitness is eliminated by using the mutation particle swarm optimization algorithm. Select more suitable particles for the next dictionary update. In addition, historical reference data are introduced into the variable particle swarm model to guide the optimization direction. The accuracy and effectiveness of the dictionary are improved. The experimental results show that the proposed algorithm is superior to the similar methods and has better noise suppression effect. At the same time, it also improves the performance of large-scale remote sensing image processing.
【作者單位】： 中國科學(xué)院遙感與數(shù)字地球研究所;中國科學(xué)院大學(xué);
【基金】：國家科技支撐計(jì)劃(2015BAJ02B00)
【分類號(hào)】：TP751

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相關(guān)期刊論文 前2條

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