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研究生:張偉民
研究生(外文):Chang, Weiming
論文名稱:一個基於相關矩陣之特徵萃取法
論文名稱(外文):A Feature Extraction Method Based on Correlation Matrix
指導教授:郭伯臣郭伯臣引用關係
指導教授(外文):Kuo, Borchen
口試委員:郭伯臣黃孝雲李政軒
口試委員(外文):Kuo, BorchenHuang, HsiaoyunLi, Chenghsuan
口試日期:2012-06-27
學位類別:碩士
校院名稱:國立臺中教育大學
系所名稱:教育測驗統計研究所
學門:教育學門
學類:教育測驗評量學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:48
中文關鍵詞:相關矩陣特徵萃取法非監督式特徵萃取譜聚類
外文關鍵詞:Correlation Matrix Feature Extraction, CMFEUnsupervised Feature ExtractionSpectral Clustering
相關次數:
  • 被引用被引用:2
  • 點閱點閱:156
  • 評分評分:
  • 下載下載:7
  • 收藏至我的研究室書目清單書目收藏:0
近年來,許多研究利用鄰近頻譜值具有高相關的特性,來設計特徵萃取或特徵選取演算法,藉以增加分群或分類之效能。然而,在使用相關矩陣做特徵萃取時會遇到兩個問題,一個是要怎麼把相關較高的頻譜群聚在一起,一個是怎麼決定門檻值(threshold values)來分割這些特徵,因此本研究發現可以使用譜聚類(spectral clustering),是一個基於相似矩陣的一個分群的演算法,可以解決上述所說的兩個問題。在本研究中,提出了一個非監督式特徵萃取法:基於譜聚類之相關矩陣特徵萃取法。基於模糊分群演算法之譜聚類將被應用至相關矩陣上進行頻譜的模糊分群,而相對應的隸屬度值可以決定在非監督式特徵萃取的轉換矩陣上。從實驗中可看出教育測驗資料集、Indian Pine Site 之子資料集、Washington DC Mall資料集與UCI資料集使用基於譜聚類之相關矩陣特徵萃取法及核化模糊演算法之分群結果可以達到最佳的分群正確率。
Recently, the high correlation property between neighboring bands is usually used for dimension reduction on data clustering or classification by grouping similar bands. However, there are two main difficulties. One is how to cluster similar bands based on the correlation matrix of bands. The other one is how to determine the thresholds for splitting the similar bands. Fortunately, the spectral clustering, a clustering algorithm based on a similarity matrix, can be used to solve the two problems simultaneously. In this study, we propose an unsupervised feature extraction method based on the correlation matrix of bands. The spectral clustering based on fuzzy c-means is applied to the correlation matrix of bands (CMFESC), and the corresponding membership values determine the transformation matrix. Experimental results on the educational measurement dataset, Indian Pine Site dataset, Washington DC Mall dataset, and some UCI data sets, show that the proposed method achieves good segmentation performance compared with principal component analysis (PCA) and independent component analysis (ICA).
摘要 I
ABSTRACT II
目錄 III
表目錄 V
圖目錄 VI
第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 4
第二章 文獻探討 5
第一節 特徵萃取 5
壹、主成份分析 5
貳、獨立成份分析 6
第二節 模糊分群演算法與其核化版本 7
壹、模糊分群演算法 7
貳、核化模糊分群演算法 8
第三節 譜聚類 11
第三章 基於譜聚類之相關矩陣特徵萃取法 18
第一節 基於模糊分群演算法之譜聚類 18
第二節 基於譜聚類之相關矩陣特徵萃取法 19
第四章 實驗設計 21
第一節 資料描述 21
壹、教育測驗資料 21
貳、Indian Pine Site影像資料 22
參、Washington DC Mall影像資料 25
肆、UCI資料集 26
第二節 實驗描述 27
第五章 實驗結果 28
第一節 教育測驗資料集 28
第二節 Indian Pine Site資料集的實驗結果 30
壹、實驗結果一 30
貳、實驗結果二 31
參、實驗結果三 32
第三節 Washington DC Mall 資料集 34
第四節 UCI 資料集 35
第六章 結論與未來發展 37
參考文獻 38
中文部分 38
英文部分 38
附錄一 微分四則運算專家知識結構 44
附錄二 教育測驗資料試題 45

中文部分
黃文俊(2009)。模糊權重分群演算法。國立臺中教育大學,台中市。
英文部分
Acito, N., Corsini, G., & Diani, M. (2003). An unsupervised algorithm for hyperspectral image segmentation based on the Gaussian mixture model, IEEE Geoscience and Remote Sensing Symposium IGARSS, 6, 3745-3747.

Bach, F. & Jordan, M. (2004). Learning spectral clustering, in S. Thrun, L. Saul, and B. Schölkopf (Eds.), Advances in Neural Information Processing Systems, vol. 16 (NIPS), 305-312, Cambridge, MA: MIT Press.

Bezdek, J. C. (1973). Fuzzy mathematics in pattern classfication. PhD thesis, Applied Math. Center, Cornell University, Ithaca.

Bioucas-Dias, J. M. & Nascimento, J. M. P. (2008). Hyperspectral subspace identification. IEEE Transactions on Geoscience and Remote Sensing, 46(8), 2435-2445.

Bruzzone, L. & Persello, C. (2009). A novel context-sensitive semisupervised SVM classifier robust to mislabeled training samples. IEEE Transactions on Geoscience and Remote Sensing, 47(7), 2142-2154.
Camps-Valls, G., Gomez-Chova, L., Munoz-Mari, J., Vila-Frances, J., & Calpe-Maravilla, J. (2006). Composite kernels for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing Letters, 3(1), 93-97.

Camps-Valls, G., Marsheva, T. V. B., & Zhou, D.Y. (2007). Semi-supervised graph-based hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 45(10), 3044-3054.

Cariou, C. Chehdi, K., & Moan, S. L. (2011). BandClust: an unsupervised band reduction method for hyperspectral remote sensing. IEEE Geoscience and Remote Sensing Letters, 8(3), 565-569.

Chang, C. I. & Du, Q. (2004). Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 42(3), 608-619.

Chatzis, S. & Varvarigou, T. (2009). Factor analysis latent subspace modeling and robust fuzzy clustering using t-distributions. IEEE Transactions on Fuzzy Systems, 17(3), 505-517.

Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition. Academic Press, San Diego, CA, 2nd edition.

Gittins, C., Konno, D., Hoke, M., & Ratkowski, A. (2008). Some effects of image segmentation on subspace-based and covariance-based detection of anomalous sub-pixel materials, International Journal of High Speed Electronics and Systems, 18(2), 349 - 367.

Goetz, A. F. H. (2009). Three decades of hyperspectral remote sensing of the earth: a personal view. Remote Sensing of Environment, 113, 5-16.

Grahn, H. & Geladi, P. (2007). Techniques and Applications of Hyperspectral Image Analysis. Chichester, U.K.: Wiley.

Gualtieri, J. A., Chettri, S. R., Cromp, R. F., & Johnson, L. F. (1999). Support vector machine classifiers as applied to AVIRIS data. presented at the 1999 Airborne Geoscience Workshop.

Frank, A. & Asunction, A. (2010). UCI Machine Learning Repository, from http://archive.ics.uci.edu/ml

Hughes, G. F. (1968). On the mean accuracy of statistical pattern recognizers. IEEE Transactions Information Theory, 14(1), 55-63.

Honda, K., Notsu, A., & Ichihashi, H. (2010). Fuzzy PCA-guided robust k-means clustering. IEEE Transactions on Fuzzy Systems, 18(1), 67-79.

Hyvrinen, A., Karhunen, J., & Oja, E. (2001). Independent copmonent analysis, John Wiley and Sons, New York.

Jain, A. & Zongker, D. (1997). Feature selection: evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(2), 153-158.

Jia, X. & Richards, J. A. (1994). Efficient maximum likelihood classification for imaging spectrometer data sets. IEEE Transaction on Geoscience and Remote Sensing, 32, 274-281.

Jlliffe, I. (1986) Principal Component Analysis. New York: Springer-Verlag.

Kuo, B. C. & Chang, K. Y. (2007). Feature extractions for small sample size classification problem. IEEE Transactions on Geoscience and Remote, 45(3), 756-764.

Kuo, B. C., Li, C. H., & Yang J. M. (2009). Kernel nonparametric weighted feature extraction for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 47(4), 1139-1155.

Landgrebe, D. A. (2003). Signal Theory Methods in Multispectral Remote Sensing. John Wiley and Sons, Hoboken, NJ: Chichester.

Lee, C. & Landgrebe, D. A. (1993). Analyzing high-dimensional multispectral data,” IEEE Transactions on Geoscience and Remote Sensing, 31(4), 792-800.

Li, C. H., Lin, C. T., Kuo, B. C., & Chu, H. S. (2010). An automatic method for selecting the parameter of the RBF kernel function to support vector machines.Proceedings of International Geosciences and RemoteSensing Symposium, 836-839.

Lin, C. T. & George Lee, C. S. (1996). Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice Hall.

Melgani, F. & Bruzzone, L. (2004). Classification of Hyperspectral Remote Sensing Images With Support Vector Machines. IEEE Transactions on Geoscience and Remote Sensing, 42(8), 1778-1790.

Nasser, A. & Hamad, D. (2006). K-means clustering algorithm in projected spaces. in Proc. 9th Int. Conf. Inf. Fusion, 1–6.

Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On spectral clustering: analysis and an algorithm. in Proc. NIPS, vol. 14, Vancouver, BC, Canada: MIT Press.

Sebastiano, B.S. & Gabriele, M. (2007). Extraction of spectral channels from hyperspectral images for classification purposes. IEEE Transactions on Geoscience and Remote, 45(2), 484-495.

Shi, J. & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine, 22(8), 888-905.

Theodoridis, S. & Koutroumbas, K. (2006). Pattern Recognition. Academic Press, 3rd edition, Inc. Orlando, FL, USA.

Weike, K., Azad, P., & Dillmann, R. (2006). Fast and robust feature-based recognition of multiple objects. in Proc. 6th IEEE-RAS Int. Conf. Humanoid Robots, 264–269.

Wu, Z. D., Xie, W. X., & Yu, J. P. (2003). Fuzzy C-means clustering algorithm based on kernel method. proceedings. Fifth International Conference on Computational Intelligence and Multimedia Applications, 49-54.

Zubko, V., Kaufman, Y. J., Burg R. I., & Martins, J. V. (2007). Principal component analysis of remote sensing of aerosols over oceans. IEEE Transactions on Geoscience and Remote Sensing, 45(3), 730-745.

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