降維（DR）和數(shù)據(jù)分類是兩個(gè)最重要的機(jī)器學(xué)習(xí)任務(wù),用于許多模式識(shí)別應(yīng)用,如人臉識(shí)別,醫(yī)學(xué)成像,指紋識(shí)別,語(yǔ)音識(shí)別等。鄰域保留策略應(yīng)用在許多著名的算法中,例如鄰域保持嵌入（NPE）,局部保留投影（LPP）和k最近鄰規(guī)則（KNN）。但是這些算法對(duì)參數(shù)設(shè)置非常敏感。例如NPE和LPP對(duì)鄰域大小的參數(shù)非常敏感,這降低了降維的性能。此外,現(xiàn)有的多種DR方法通常利用單個(gè)圖來保持鄰域關(guān)系,這種區(qū)分不適合于多視圖數(shù)據(jù)集的降維。此外KNN的分類性能受鄰域大小k和現(xiàn)有異常值的影響很大。因此本文設(shè)計(jì)了基于近鄰集成保持策略的降維和分類方法研究,旨在減少NPE,LPP和KNN中的上述近鄰約束。在第一種DR方法中,我們提出了一種稱為加權(quán)鄰域保持集成嵌入（WNPEE）的新型DR方法。與NPE不同,所提出的WNPEE構(gòu)造了多個(gè)近鄰圖的集成。通過近鄰圖的集成構(gòu)建,WNPEE可以通過聯(lián)合優(yōu)化方式獲得最優(yōu)嵌入圖的低維投影。對(duì)ORL,Georgia Tech,CMU PIE和Yale四種人臉數(shù)據(jù)集的實(shí)驗(yàn)表明,WNPEE實(shí)現(xiàn)了比NPE和其他實(shí)驗(yàn)對(duì)比的DR方法更高的識(shí)別率。此外,與NPE和其他相關(guān)的DR算法相比,所提出的WNPE... 

【文章來源】：江蘇大學(xué)江蘇省

【文章頁(yè)數(shù)】：146 頁(yè)

【學(xué)位級(jí)別】：博士

【文章目錄】：
ABSTRACT
摘要
Chapter1 Introduction
    1.1 Background
    1.2 Dimension reduction
    1.3 Challenges in neighborhood related DR techniques
    1.4 Data classification
    1.5 Challenges in nearest neighbor classifiers
    1.6 Research contributions
    1.7 The organization of thesis
Chapter2 Related Work
    2.1 Related DR techniques
        2.1.1 Traditional linear DR techniques
            2.1.1.1 Principal components analysis
            2.1.1.2 Linear discriminant analysis
        2.1.2 Local nonlinear DR approaches
            2.1.2.1 Local linear embedding
            2.1.2.2 Laplacian eigenmaps
        2.1.3 Linear approximations to local nonlinear DR techniques
            2.1.3.1 Linearity preserving projection
            2.1.3.2 Neighborhood preserving embedding
        2.1.4 Multi-view learning techniques
    2.2 Nearest neighborhood based classifiers
        2.2.1 A local mean-based nonparametric classifier
        2.2.2 Nearest centroid neighbor classifier
        2.2.3 The k-nearest centroid neighbor classifier
        2.2.4 A local mean-based k-nearest centroid neighbor classifier
        2.2.5 Harmonic mean distance
        2.2.6 A new k-harmonic nearest neighbor classifier based on the multi-local means
Chapter3 Weighted Neighborhood Preserving Ensemble Embedding
    3.1 Introduction
    3.2 The proposed WNPEE method
        3.2.1 Obtaining weight parameter in proposed WNPEE
    3.3 Experimental results and analysis
        3.3.1 Experiments on ORL face database
        3.3.2 Experiments on GT face database
        3.3.3 Experiments on CMU PIE face database
        3.3.4 Experiments on YALE face database
        3.3.5 Analysing the sensitivity to neighborhood size parameter
        3.3.6 Computation complexity
    3.4 Brief summary
Chapter4 Generalized Multi-manifold Graph Ensemble Embedding for Multi-View Dimensionality Reduction
    4.1 Introduction
    4.2 Proposed Methods
        4.2.1 Obtaining Parameter and in proposed methods
        4.2.2 Classification difference between LPP,EGLPP and MLGEE
    4.3 Experimental Results
        4.3.1 Dataset description
        4.3.2 Experimental settings
        4.3.3 Result discussions and comparisons
            4.3.3.1 Comparison between EGLPP and LPP
            4.3.3.2 Parameter selection in MGLPP
            4.3.3.3 Experimental results for MLGEE
        4.3.4 Computational complexity
    4.4 Brief summary
Chapter5 A New Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance
    5.1 Introduction
    5.2 Description of LMKHNCN
    5.3 Comparison with traditional KNN based classifiers
    5.4 Experiment results and discussion
        5.4.1 Performance evaluation
        5.4.2 Description of the datasets
        5.4.3 Experimental procedure
        5.4.4 Analysing the error rates results with corresponding k value
        5.4.5 Results of the sensitivity to the neighborhood size k
        5.4.6 Analysing the effect of distance on classification performance
        5.4.7 Analysing the computational complexity
        5.4.8 Evaluation of experimental results
    5.5 Brief summary
Chapter6 General Conclusions and Future Works
    6.1 General conclusions
    6.2 Future works
References
Acknowledgements
Publications


【參考文獻(xiàn)】：
期刊論文
[1]一種有監(jiān)督的稀疏保持近鄰嵌入算法[J]. 鄭豪,金忠.  計(jì)算機(jī)工程. 2011(16)



本文編號(hào)：3549089