基于模糊粗糙C均值的图像大数据CNN聚类与分类

发布时间：2021-08-28 09:50

　　深度学习（DL）作为规模图像大数据的聚类与分类的一种有效工具,展现了其解决无监督和半监督条件下的聚类与分类算法表示问题的无限潜能。卷积神经网络如今被运用在许多先进的聚类分类算法中。然而,随着数字图像的不断增长,冗余的、无关的噪声样本也随之增多,从而导致了卷积神经网络的性能的逐渐下降。卷积神经网络需要大量的有类标的样本来训练网络。然而有类标的数据常常较难获得,价格昂贵且耗时。为了克服这个问题,我们该算法将卷积神经网络和模糊粗糙理论相结合提出了一种有效的针对规模图像大数据的无监督和半监督算法,这其中主要的概念是降低模糊和粗糙的不确定性,同时更特别的是利用神经网络从原始数据中去掉噪声样本。本文提出的算法如下:1.首先,我们提出一种无监督的聚类算法（FRCNN）。中心思想是利用多层卷积神经网络来学习高纬表达式,通过神经网络中的一个聚类层来初始化聚类中心。在训练过程中图像的聚类中心和表达式交替更新。FRCM用来在前向传播过程中更新聚类中心,同时利用基于随机梯度下降法的后向传播来更新神经网络权值。2.其次,本文构造了一种半监督模糊粗糙卷积神经网络（SSFRCNN）,将模糊粗糙C均值聚类和卷积神经网... 

【文章来源】：西安电子科技大学陕西省 211工程院校 教育部直属院校

【文章页数】：145 页

【学位级别】：博士

【文章目录】：
摘要
ABSTRACT
List of Abbreviations
Chapter 1 Introduction
    1.1. Rough Set Theory (RST)
    1.2. Fuzzy C-Mean Clustering (FCM)
    1.3. Fuzzy Rough C-Mean Clustering (FRCM)
    1.4. Rough Set Attributes Reduction (RSAR)
    1.5. Convolution Neural Network
    1.6. Motivations
        1.6.1. Motivation of Fuzzy Rough C-Mean Based Unsupervised CNN Clustering forLarge-Scale Image Data
        1.6.2. Motivation of the A Semi-Supervised CNN with Fuzzy Rough C-Mean forImage Classification
        1.6.3. Motivation of Rough-KNN Noise-Filtered Convolutional Neural Network forImage Classification
        1.6.4. Motivation of Rough Noise-Filtered Easy Ensemble for Software FaultPrediction
    1.7. Contributions and Organization
        1.7.1. Contribution of Fuzzy Rough C-Mean Based Unsupervised CNN Clusteringfor Large-Scale Image Data
        1.7.2. Contribution of a Semi-Supervised CNN with Fuzzy Rough C-Mean for ImageClassification
        1.7.3. Contribution of Rough-KNN Noise-Filtered Convolutional Neural Networkfor Image Classification
        1.7.4. Contribution of Rough Noise-Filtered Easy Ensemble for Software FaultPrediction
        1.7.5. Organization
Chapter 2 Fuzzy Rough C-Mean Based Unsupervised CNN Clustering for Large-ScaleImage Data
    2.1. Introduction
    2.2. Fuzzy Rough C-Mean Based Unsupervised CNN Clustering
        2.2.1. The Problem of Deep-Learning-Based Clustering
        2.2.2. Background of Fuzzy Rough C-Mean (FRCM)
        2.2.3. Theoretical description of Proposed Approach
            2.2.3.1. FRUCNN Clustering Architecture
            2.2.3.2. Joint Clustering and Representation Learning
                2.2.3.2.1. Pre-Processing Data for UCNN
                2.2.3.2.2. Cluster Centroid Updating
                2.2.3.2.3. Representation Learning
    2.3. Experiments
        2.3.1. Data Preparation
        2.3.2. Performance Measure
        2.3.3. Comparison Schemes
        2.3.4. Implementation Details
        2.3.5. Experimental Design
            2.3.5.1. Computational Time Comparison
            2.3.5.2. Performance on Number of Cluster (k)
            2.3.5.3. Performance on Number of Epochs
    2.4. Threats to Validity
    2.5. Chapter Summary
Chapter 3 A Semi-Supervised CNN with Fuzzy Rough C-Mean for Image Classification
    3.1. Introduction
    3.2. A Semi-Supervised Fuzzy Rough Convolutional Neural Network (SSFRCNN)
        3.2.1. Framework of Our Approach
        3.2.2. Theoretical description of Proposed Approach
        3.2.3. Semi-Supervised Fuzzy Rough Convolution Neural Network (FRCNN)Training
        3.2.4. Mathematical Description
    3.3. Experiments
        3.3.1. Data Preparation
        3.3.2. Experimental Setup
        3.3.3. Experiment Result and Analysis
        3.3.4. Time Complexity
        3.3.5. Convergence Analysis of Semi-Supervised Fuzzy Rough ConvolutionalNeural Network (SSFRCNN)
    3.4. Chapter Summary
Chapter 4 Rough-KNN Noise-Filtered Convolutional Neural Network for ImageClassification
    4.1. Introduction
    4.2. Rough Set Theory Based 2d-Reduction Method
        4.2.1. Framework of Our Approach
        4.2.2. Theoretical description of Proposed Approach
    4.3. Experiments
        4.3.1. Data Preparation
        4.3.2. Implementation of Experiment
        4.3.3. Experiment Design&Analysis
            4.3.3.1. MNIST
            4.3.3.2. CIFAR-10
            4.3.3.3. YTF (Youtube-Face)
    4.4. Chapter Summary
Chapter 5 Rough Noise-Filtered Easy Ensemble for Software Fault Prediction
    5.1. Introduction
    5.2. Rough Noise-Filtered Easy Ensemble for Software Fault Prediction
        5.2.1. Framework of Our Approach
        5.2.2. Theoretical description of Proposed Approach
            5.2.2.1. Information Gain (IG) based Feature Selection
                5.2.2.1.1. Information Gain (IG)
                5.2.2.1.2. Symmetrical Uncertainty (SU)
            5.2.2.2. Rough- KNN Noise Filter (RK- Filter)
            5.2.2.3. Rough-KNN Noise Filtered Easy Ensemble (RKEE)
    5.3. Experiments
        5.3.1. Data Preparation
        5.3.2. Performance Measure
        5.3.3. Classification Models
        5.3.4. Experimental Design
        5.3.5. Result and Analysis
            5.3.5.1. Analysis of X-All verses X
            5.3.5.2. The effectiveness of Noise-Filter through KNN rule
            5.3.5.3. Impact of Rough set theory with Noise-Filter
            5.3.5.4. The impact of the combination of feature selection with Rough Noise-FilterEasy Ensemble
            5.3.5.5. Relationship between the performance and imbalanced ratio
            5.3.5.6. Comparison of different Schemes
    5.4. Chapter Summery
Chapter 6 Concluding Remarks and Future Work
    6.1. Concluding Remarks
    6.2. Future work
References
Acknowledgements
Bibliography



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