基于深度卷積神經(jīng)網(wǎng)絡(luò)的特征優(yōu)化與分類識別方法研究
發(fā)布時(shí)間：2018-08-31 10:01
【摘要】：模式識別是人工智能領(lǐng)域內(nèi)的一個(gè)重要分支,能夠讓機(jī)器觀察周圍環(huán)境并學(xué)習(xí)對不同的模式做出相應(yīng)的判斷。特征的好壞是模式識別系統(tǒng)發(fā)揮性能的關(guān)鍵,因此,需要一種通用的特征優(yōu)化方法。本課題立足于模式識別問題中特征優(yōu)化與分類識別兩個(gè)方面,在使用深度卷積神經(jīng)網(wǎng)絡(luò)(Deep Convolutional Neural Network,DCNN)進(jìn)行分析的基礎(chǔ)上,研究在不同實(shí)際問題下如何對特征進(jìn)行優(yōu)化,從而對達(dá)到提高識別率的效果,并加深對于網(wǎng)絡(luò)行為的理解。其主要研究內(nèi)容包括:(1)在分析基于DCNN的二分類問題的特征優(yōu)化方法的同時(shí),結(jié)合腦機(jī)接口(Brain Computer Interface,BCI)中二分類運(yùn)動想象的具體例子,提出基于DCNN的共同空間模式(Common Spatial Pattern,CSP)自適應(yīng)特征優(yōu)化方法。在原始腦電信號經(jīng)過預(yù)處理的基礎(chǔ)上,通過CSP空間變換獲得其相應(yīng)的特征矩陣。應(yīng)用DCNN對特征矩陣進(jìn)行學(xué)習(xí),對收斂后的DCNN網(wǎng)絡(luò)全連接層的權(quán)值進(jìn)行分析,根據(jù)網(wǎng)絡(luò)學(xué)習(xí)特性定義CSP矩陣特征篩選準(zhǔn)則,得到降維高效的EEG特征集F,計(jì)算特征集F規(guī)模構(gòu)建CNN分類器。我們工作在BCI2005Ⅳa競賽數(shù)據(jù)集上進(jìn)行實(shí)驗(yàn)測試,獲得88.3%的識別準(zhǔn)確率。本方法與CSP方法的改進(jìn)方法s CSP和KLCSP方法在相同數(shù)據(jù)集上進(jìn)行測試,平均識別準(zhǔn)確率分別提升了3.2%和2.4%。(2)在二分類問題的基礎(chǔ)上,進(jìn)一步分析基于DCNN的多分類問題的特征優(yōu)化方法的同時(shí),針對語音信號認(rèn)知中構(gòu)建實(shí)際系統(tǒng)中需要優(yōu)選有效特征的問題,提出了一種基于DCNN的特征降維方法。對原始語音情感數(shù)據(jù)提取大量特征,應(yīng)用DCNN對特征矩陣進(jìn)行學(xué)習(xí),提取權(quán)值并根據(jù)網(wǎng)絡(luò)學(xué)習(xí)特性定義特征篩選準(zhǔn)則MCFR-DCNN,計(jì)算對比每類特征激活權(quán)值的不同,得到降維高效的語音情感認(rèn)知特征集F。在中國科學(xué)院自動化研究所提供的多模態(tài)情感數(shù)據(jù)庫CHEAVD上,我們提取全部八類情感數(shù)據(jù)進(jìn)行了實(shí)驗(yàn)測試,使用全體特征集構(gòu)建DCNN分類器相比基線類平均識別錯(cuò)誤率減少了2.1%,而本方法得到的降維后特征集F通過相同的DCNN分類器相比基線類平均錯(cuò)誤率較少了9.4%。本方法僅使用原特征集15%的特征,不僅減少了構(gòu)筑實(shí)際語音情感識別系統(tǒng)的復(fù)雜程度,還使得識別錯(cuò)誤率還有所減小。(3)在進(jìn)一步分析使用DCNN對基于復(fù)雜特征的多分類問題進(jìn)行研究的同時(shí),結(jié)合具體的物體識別的例子,提出了基于DCNN的權(quán)值矩陣特性的復(fù)雜特征優(yōu)化方法。使用DCNN對原始數(shù)據(jù)進(jìn)行學(xué)習(xí)后提取全連接層權(quán)值,根據(jù)網(wǎng)絡(luò)學(xué)習(xí)特性定義特征價(jià)值矩陣,進(jìn)而將原始數(shù)據(jù)與特征價(jià)值矩陣進(jìn)行線性組合得到新的數(shù)據(jù)集。在大規(guī)模圖像數(shù)據(jù)庫Image Net上進(jìn)行了實(shí)驗(yàn)測試,分別使用3種分類器,相對于全部數(shù)據(jù)集,使用經(jīng)過特征優(yōu)化后的數(shù)據(jù)集在識別率上有所提高。本文研究針對不同數(shù)據(jù)的不同特征形式,采用DCNN網(wǎng)絡(luò)學(xué)習(xí)特性進(jìn)行特征二次優(yōu)選與降維,為模式識別領(lǐng)域中的特征優(yōu)化與分類識別問題提供了一個(gè)新的思路。
[Abstract]:Pattern recognition is an important branch in the field of artificial intelligence, which allows machines to observe the environment and learn to make corresponding judgments on different patterns. The quality of features is the key to the performance of pattern recognition system. Therefore, a general feature optimization method is needed. In two aspects of classification and recognition, based on the analysis of Deep Convolutional Neural Network (DCNN), this paper studies how to optimize the features under different practical problems, so as to improve the recognition rate and deepen the understanding of network behavior. The feature optimization method based on DCNN for binary classification problem is proposed. Combined with the specific examples of binary classification motion imagery in Brain Computer Interface (BCI), an adaptive feature optimization method based on DCNN Common Spatial Pattern (CSP) is proposed. The corresponding characteristic matrix is obtained by space transformation. DCNN is used to study the characteristic matrix, and the weights of the full connection layer of the convergent DCNN network are analyzed. According to the learning characteristics of the network, the feature selection criterion of CSP matrix is defined. The dimension-reduced and efficient EEG feature set F is obtained, and the scale of feature set F is calculated to construct the CNN classifier. We work in BCI 2005 IV. The improved CSP and KLCSP methods are tested on the same data set, and the average recognition accuracy is improved by 3.2% and 2.4%. (2) Based on the binary classification problem, the feature optimization of DCNN-based multi-classification problem is further analyzed. Meanwhile, a feature reduction method based on DCNN is proposed to optimize the effective features in speech recognition system. A large number of features are extracted from the original speech emotion data. DCNN is used to learn the feature matrix, extract the weights and define the feature selection criterion MCFR-DC according to the network learning characteristics. Based on the multi-modal emotion database CHEAVD provided by the Institute of Automation of the Chinese Academy of Sciences, we extracted all eight kinds of emotion data for experimental testing, and constructed DCNN classifier with all feature sets to compare the baseline class average. The recognition error rate is reduced by 2.1%, and the average error rate of feature set F is 9.4% less than that of baseline class by using the same DCNN classifier. This method uses only 15% features of the original feature set, which not only reduces the complexity of constructing actual speech emotion recognition system, but also reduces the recognition error rate. Further more, this paper analyzes the use of DCNN to study the multi-classification problem based on complex features, and proposes a complex feature optimization method based on DCNN weight matrix characteristics with specific object recognition examples. Matrix is used to combine the original data with the eigenvalue matrix linearly to get a new data set. Experiments are carried out on the large-scale image database Image Net. Three classifiers are used to improve the recognition rate of the optimized data set. With different feature forms, DCNN network learning characteristics are used to optimize and reduce the dimensions of features, which provides a new idea for feature optimization and classification in the field of pattern recognition.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP18
本文編號：2214652
[Abstract]:Pattern recognition is an important branch in the field of artificial intelligence, which allows machines to observe the environment and learn to make corresponding judgments on different patterns. The quality of features is the key to the performance of pattern recognition system. Therefore, a general feature optimization method is needed. In two aspects of classification and recognition, based on the analysis of Deep Convolutional Neural Network (DCNN), this paper studies how to optimize the features under different practical problems, so as to improve the recognition rate and deepen the understanding of network behavior. The feature optimization method based on DCNN for binary classification problem is proposed. Combined with the specific examples of binary classification motion imagery in Brain Computer Interface (BCI), an adaptive feature optimization method based on DCNN Common Spatial Pattern (CSP) is proposed. The corresponding characteristic matrix is obtained by space transformation. DCNN is used to study the characteristic matrix, and the weights of the full connection layer of the convergent DCNN network are analyzed. According to the learning characteristics of the network, the feature selection criterion of CSP matrix is defined. The dimension-reduced and efficient EEG feature set F is obtained, and the scale of feature set F is calculated to construct the CNN classifier. We work in BCI 2005 IV. The improved CSP and KLCSP methods are tested on the same data set, and the average recognition accuracy is improved by 3.2% and 2.4%. (2) Based on the binary classification problem, the feature optimization of DCNN-based multi-classification problem is further analyzed. Meanwhile, a feature reduction method based on DCNN is proposed to optimize the effective features in speech recognition system. A large number of features are extracted from the original speech emotion data. DCNN is used to learn the feature matrix, extract the weights and define the feature selection criterion MCFR-DC according to the network learning characteristics. Based on the multi-modal emotion database CHEAVD provided by the Institute of Automation of the Chinese Academy of Sciences, we extracted all eight kinds of emotion data for experimental testing, and constructed DCNN classifier with all feature sets to compare the baseline class average. The recognition error rate is reduced by 2.1%, and the average error rate of feature set F is 9.4% less than that of baseline class by using the same DCNN classifier. This method uses only 15% features of the original feature set, which not only reduces the complexity of constructing actual speech emotion recognition system, but also reduces the recognition error rate. Further more, this paper analyzes the use of DCNN to study the multi-classification problem based on complex features, and proposes a complex feature optimization method based on DCNN weight matrix characteristics with specific object recognition examples. Matrix is used to combine the original data with the eigenvalue matrix linearly to get a new data set. Experiments are carried out on the large-scale image database Image Net. Three classifiers are used to improve the recognition rate of the optimized data set. With different feature forms, DCNN network learning characteristics are used to optimize and reduce the dimensions of features, which provides a new idea for feature optimization and classification in the field of pattern recognition.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP18
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