多媒体数据分析的多视图流形表示研究

发布时间：2024-04-20 01:10

　　在机器学习领域中普遍面临处理大量且高维的多媒体数据问题。并且,如何从具有多样性和非线性的多媒体数据中提取有效的鉴别性特征,是特征提取算法中具有挑战性的课题。本文对以上问题进行了研究,其核心思想是利用高维数据在实际应用中往往具有低维的特点,将数据的几何结构表示为流形图结构并进行分析。论文具体介绍了三种新的多媒体数据分析方法,并取得了显著的进展。其中包括引入了多流形嵌入的字典诱导最小二乘框架,引入了图嵌入的广义多字典最小二乘框架,以及通过保持PCA框架的全局和局部结构进行流形对齐。第一种方法扩展了主成分分析(PCA)的概念,通过最小化最小二乘重构误差思想保持数据全局结构,并引入分布字典对丢失和噪声数据点的离群分布对数据结构重构。接着,通过多流形嵌入保持纯净的局部结构。因此,这种方法可以在低维投影中获得鉴别信息,同时保持全局和局部结构的平衡。我们提出的方法在多媒体数据分析方面进行了大量实验并与目前最先进方法相比表明该方法具有更好的性能。进一步的,在此基础上对第一种方法进行扩展,我们提出使用包含多个字典的第二种方法。在多视图数据的情况下,多字典进一步增强了对噪声和冗余数据点的识别。接着,根据两...

【文章页数】：155 页

【学位级别】：博士

【文章目录】：
ABSTRACT
摘要
CHAPTER 1 INTRODUCTION
    1.1 BACKGROUND OF MULTIMEDIA DATA ANALYSIS
    1.2 THE STUDY SIGNIFICANCE
    1.3 CHALLENGES IN MULTIMEDIA DATA ANALYSIS
    1.4 CONTRIBUTIONS OF THE DISSERTATIQN
    1.5 ORGANIZATION OF THE DISSERTATION
CHAPTER 2 RELATED WORK
    2.1 REPRESENTATION LEARNING
        2.1.1 Dimensionality Reduction and Graph Embedding Techniques
            2.1.1.1 Global Structures Preserving Techniques
            2.1.1.2 Local Structures Preserving Techniques
            2.1.1.3 Multi-view Learning Techniques
        2.1.2 Manifold Alignment Techniques
CHAPTER 3 DICTIONARY-INDUCED LEAST SQUARES FRAMEWORK WITHMULTI-MANIFOLD EMBEDDINGS
    3.1 INTRODUCTION
    3.2 PROPOSED DLSME
        3.2.1 Achieving Lower Dimensions in DLSME
        3.2.2 Obtaining Parameter γ in our Proposed DLSME
    3.3 EXPERIMENTAL RESULTS
        3.3.1 Dataset Description
        3.3.2 Experimental Settings
        3.3.3 Results Discussions and Comparisons
            3.3.3.1 Comparison between DLS and PCA
            3.3.3.2 Web Image Annotation
            3.3.3.3 Visual Recognition
                (1) Digit Recognition
                (2) Object Recognition
            3.3.3.4 Computation Complexity
            3.3.3.5 Parameters α and r of DLSME
    3.4 SUMMARY
CHAPTER 4 A GENERALIZED MULTI-DICTIONARY LEST SQUARESFRAMEWORK REGULARIZED WITH MULTI-GRAPH EMBEDDINGS
    4.1 INTRODUCTION
    4.2 THE PROPOSED MD-MGE METHODS
        4.2.1 Obtaining Low Dimensional Projections in the Proposed Methods
        4.2.2 Obtaining α and β in the Proposed Methods
    4.3 EXPERIMENTAL RESULTS AND ANALYSIS
        4.3.1 Experimental Setting
        4.3.2 Handwritten Numerals Recognition
        4.3.3 Object Recognition
        4.3.4 Face Recognition
            4.3.4.1 Experiments on the ORL Dataset
            4.3.4.2 Experiments on the Extended YaleB Dataset
        4.3.5 Speech Recognition
        4.3.6 Computational Complexity and Time
        4.3.7 Control Parameters c & r of MD-MGE
        4.3.8 Comparison of MD-MGE and DLSME
        4.3.9 Evaluation of Experimental Results
    4.4 SUMMARY
CHAPTER 5 MANIFOLD ALIGNMENT VIA GLOBAL AND LOCAL STRUCTURESPRESERVING PCA FRAMEWORK
    5.1 INTRODUCTION
    5.2 THE PROPOSED MAPGL METHOD
        5.2.1 Global and Local Structures Preserving PCA Framework
        5.2.2 Optimizing MAPGL
        5.2.3 Obtaining Parameters γ and β in MAPGL
    5.3 EXPERPIMENTS
        5.3.1 Datasets Description
        5.3.2 Experimental Settings
        5.3.3 Alignment Experiments
            5.3.3.1 Protein Manifolds Alignment
            5.3.3.2 Rotated Objects Alignment
            5.3.3.3 Head Pose Images Alignment
            5.3.3.4 Image and Text Alignment
        5.3.4 Experiments on Visual Recognition
            5.3.4.1 Objects Recognition
                (i) Handwritten Numerals Recognition
                    (a) Experiments on MFD Dataset
                    (b) Experiments on USPS Dataset
                (ii) Face Recognition
                    (a) Experiments on YALE Dataset
                    (b) Experiments on AR Dataset
                    (c) Experiments on UMIST Dataset
        5.3.5 Effect of Parameters in MAPGL
    5.4 SUMMARY
CHAPTER 6 GENERAL CONCLUSIONS AND FUTURE WORK
    6.1 GENERAL CONCLUSIONS
    6.2 CONTRIBUTIONS
    6.3 FUTURE WORK
REFERENCES
ACKNOWLEDGEMENTS
PUBLICATIONS



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