本文選題：負(fù)熵　切入點(diǎn)：峭度　出處：《廣東工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來,線性非高斯無環(huán)模型(LiNGAM)在沒有任何先驗(yàn)知識(shí)的情況下能夠從觀察數(shù)據(jù)中完整的識(shí)別因果網(wǎng)絡(luò)而得到越來越多的關(guān)注,并在神經(jīng)科學(xué),經(jīng)濟(jì)學(xué),基因組學(xué)等領(lǐng)域得到了廣泛的應(yīng)用.Direct LiNGAM(Direct Method for Learning a Linear Non-Gaussian Structural Equation Model)框架是其中一個(gè)經(jīng)典解法,但其存在當(dāng)維度達(dá)到25維度以上時(shí),外生變量識(shí)別率低的問題,從而產(chǎn)生級(jí)聯(lián)效應(yīng),使得整個(gè)網(wǎng)絡(luò)的估計(jì)誤差隨著層數(shù)增大越來越大,并且計(jì)算復(fù)雜度達(dá)到了維度的三次方.針對(duì)以上問題,本文從三個(gè)不同的角度來研究外生變量的識(shí)別問題:(1)從局部選擇的角度出發(fā),把變量的非高斯性作為外生變量選擇的標(biāo)準(zhǔn),用負(fù)熵來度量變量的非高斯,選擇負(fù)熵最大的k個(gè)變量存入局部目標(biāo)變量集合Lv中,在集合Lv中進(jìn)一步去尋找外生變量,從而提高了外生變量的識(shí)別率.(2)從獨(dú)立性的角度出發(fā),通過引入自適應(yīng)的獨(dú)立性判定參數(shù),根據(jù)此參數(shù)來找出與其余所有變量回歸得到的殘差都獨(dú)立的變量,即為外生變量.該算法不僅避免了傳統(tǒng)算法對(duì)獨(dú)立性值差異敏感而導(dǎo)致識(shí)別率低的問題,而且也避免了不同數(shù)據(jù)集對(duì)固定獨(dú)立性參數(shù)敏感而導(dǎo)致無法識(shí)別的缺陷.(3)從估計(jì)方式的角度出發(fā),通過引入峭度的度量標(biāo)準(zhǔn),我們發(fā)現(xiàn)當(dāng)干擾變量服從獨(dú)立同分布時(shí),外生變量是具有最大的峭度值,基于此特征我們提出了一種直接識(shí)別外生變量的方法,該算法不僅是一種直接量化的關(guān)系,并且計(jì)算復(fù)雜度僅僅為維度的二次方.本文的研究成果不僅豐富了LiNGAM模型的研究,而且在一定程度上為外生變量識(shí)別提供了新的方法支持。
[Abstract]:In recent years, the linear non-ring Gao Si model LiNGAM has gained more and more attention in neuroscience, economics, and the complete identification of causal networks from observational data without any prior knowledge. Direct LiNGAM(Direct Method for Learning a Linear Non-Gaussian Structural Equation Model framework has been widely used in genomics and other fields, but it has the problem of low recognition rate of exogenous variables when the dimension reaches above 25 dimension, which results in cascade effect. The estimation error of the whole network increases with the increase of the number of layers, and the computational complexity reaches the third power of the dimension. In this paper, we study the problem of identification of exogenous variables from three different angles. (1) from the point of view of local selection, we take the non-#china_person0# nature of variables as the criterion for the selection of exogenous variables, and use negative entropy to measure the non-#china_person1# of variables. Select k variables with maximum negative entropy into the set of local objective variables LV, and further search for exogenous variables in the set LV, thus improving the recognition rate of exogenous variables. By introducing an adaptive independence decision parameter, the variables which are independent of the residuals obtained from the regression of all the other variables are found according to this parameter. This algorithm not only avoids the problem that the traditional algorithm is sensitive to the difference of independence value, which leads to low recognition rate. In addition, the defect of different data sets, which is sensitive to fixed independence parameters, is avoided. (3) from the point of view of estimation method, we find that when the interference variables are distributed independently, the kurtosis metric is introduced. Exogenous variables have the largest kurtosis value. Based on this feature, we propose a method to directly identify exogenous variables. This algorithm is not only a direct quantization relation, The computational complexity is only the quadratic of the dimension. The results of this paper not only enrich the research of LiNGAM model, but also provide a new method for the identification of exogenous variables to some extent.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP301.6

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 蔡瑞初;陳薇;張坤;郝志峰;;基于非時(shí)序觀察數(shù)據(jù)的因果關(guān)系發(fā)現(xiàn)綜述[J];計(jì)算機(jī)學(xué)報(bào);2017年06期

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本文編號(hào)：1618259