本文選題：數(shù)據(jù)降維 + 拉普拉斯特征映射��； 參考：《暨南大學(xué)》2017年碩士論文

【摘要】：首先,針對(duì)拉普拉斯特征映射的新增樣本點(diǎn)延拓問(wèn)題,提出一種基于鄰域信息的新增樣本點(diǎn)延拓方法:假設(shè)新增樣本點(diǎn)與鄰域保持線性關(guān)系,使用稀疏編碼方法求解線性系數(shù),再由這些系數(shù)在低維空間重構(gòu)得到新增樣本點(diǎn)的低維表示。實(shí)驗(yàn)結(jié)果表明,與基于全局信息的稀疏編碼重構(gòu)方法相比,基于鄰域信息的稀疏編碼重構(gòu)算法使用更少的時(shí)間取得更高的分類準(zhǔn)確率。此外,該方法可以推廣至其他非線性降維方法的新增樣本點(diǎn)問(wèn)題。其次,針對(duì)降維問(wèn)題,提出同時(shí)從類標(biāo)簽和高維數(shù)據(jù)結(jié)構(gòu)學(xué)習(xí)低維表示的監(jiān)督學(xué)習(xí)降維方法,使用兩步交替迭代法求解相應(yīng)的優(yōu)化問(wèn)題,給出了該方法有解并收斂的證明。與其他有監(jiān)督的數(shù)據(jù)降維方法對(duì)比,本文的算法在實(shí)驗(yàn)中表現(xiàn)出其優(yōu)越性。
[Abstract]:First, an additional sample point extension method based on neighborhood information is proposed for the new sample point extension problem of Laplasse's feature mapping. It is assumed that the new sample points keep linear relationship with the neighbourhood, and the linear coefficients are solved by the sparse coding method, and then the low dimension representation of the new sample points is obtained by these coefficients in the low dimensional space reconstruction. The experimental results show that the sparse coding reconstruction algorithm based on neighborhood information uses less time to obtain higher classification accuracy compared with the sparse coding reconstruction method based on global information. In addition, this method can be extended to the new sample point problem of other nonlinear dimensionality reduction methods. Secondly, in view of the dimensionality reduction problem, this method is proposed at the same time. The label and high dimensional data structure learn the supervised learning reduction method of low dimension representation, use the two step alternate iterative method to solve the corresponding optimization problem, and give the proof of the solution and convergence of the method. Compared with other supervised data reduction methods, the algorithm of this paper shows its superiority in the experiment.
【學(xué)位授予單位】：暨南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP181

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本文編號(hào)：2079503