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研究生:張鍵中
研究生(外文):Chien-Chung Chang
論文名稱:縮減集及其應用之研究
論文名稱(外文):A Study on Reduced Set with Applications
指導教授:李育杰李育杰引用關係
指導教授(外文):Yuh-Jye Lee
口試委員:李育杰
口試日期:2011-07-26
學位類別:博士
校院名稱:國立臺灣科技大學
系所名稱:資訊工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:英文
論文頁數:80
中文關鍵詞:共同訓練共識訓練特徵選擇多觀點學習半監督式學習支撐向量法
外文關鍵詞:co-trainingconsensus trainingfeature selectionmulti-view learningsemi-supervised learningsupport vector machines
相關次數:
  • 被引用被引用:0
  • 點閱點閱:243
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  • 下載下載:20
  • 收藏至我的研究室書目清單書目收藏:0
為了克服傳統非線性支撐向量法(nonlinear SVMs)在處理大量資料時所遇到的計算困難,縮減集支撐向量法(reduced SVM, RSVM)利用一個比較小的長方形核矩陣(rectangular kernel matrix)取代非線性支撐向量法公式中充分稠密的正方形核矩陣(fully dense kernel matrix)。這個長方形核矩陣是利用一個從原始資料中隨機選取的資料子集合所生成的,我們稱這個資料子集合為「縮減集」(reduced set)。選擇具有代表性(representative)的縮減集能夠提升縮減集支撐向量法的效能,因此在本篇論文中,我們首先介紹三種能選擇比較具有代表性縮減集的方法,分別是漸增式縮減集支撐向量法(IRSVM)、集群縮減集支撐向量法(CRSVM)、以及系統抽樣縮減集支撐向量法(SSRSVM)。IRSVM依序尋找一個不在現有基底函數(basis function)所形成的特徵空間(feature space)中之核函數(kernel function),並將其增加到現有基底函數集之中。CRSVM則是針對各類別資料,先進行k-means的集群演算法,再將所有的群心(centroids)集合成縮減集。SSRSVM首先利用一個極小的縮減集建立一個初始的分類器(classifier),並利用此分類器對不在縮減集內的訓練資料(training data)進行分類,再選取部份被錯分之點加入縮減集,並使用新的縮減集重新訓練一個分類器。我們重複上述之程序直到滿足停止條件(stopping criteria)為止。
我們提出了一種名為 incremental forward feature selection(IFFS)的特徵(feature)選擇方法,這個方法是起源於IRSVM。它的直覺想法是,如果新的特徵會帶來最多額外的資訊量,則它將會被加到目前的特徵子集中。資訊量可以利用此一新的特徵向量與目前特徵子集所形成的行空間之距離度量。在本論文的最後,我們提出了一種基於縮減集支撐向量法的多觀點(multi-view)類型半監督式學習演算法,名為two-teachers-one-student (2T1S)。在2T1S的方法中,縮減集是在核特徵空間(feature space)中扮演觀點(view)的角色,而非如傳統多觀點演算法是在原資料空間(input space)中選擇特徵子集當作觀點。我們的2T1S結合了共同訓練(co-training)和共識訓練(consensus training)兩種概念。透過共同訓練的方法,由兩個觀點所形成的兩個分類器(老師)能夠“教導”第三個觀點所形成的分類器(學生),其中,老師與學生的角色是會輪替的。藉由共識訓練的想法,利用兩個分類器共同對未標記資料進行類別的標記所形成的共識(即標記為同一類別),能提升我們猜測未標記資料點之類別的信心。
The reduced support vector machine (RSVM) replaces the fully dense matrix with a smaller rectangular kernel matrix, which is used in the nonlinear SVM formulation to avoid the computational difficulties. This rectangular kernel matrix is generated by a uniform random subset, named reduced set. In this dissertation, we present three schemes to select a more representative reduced set for RSVM, namely incremental RSVM (IRSVM), clustering RSVM (CRSVM), and systematic sampling RSVM (SSRSVM). The IRSVM sequentially adds a kernel function to the current basis function set only when the function is dissimilar to the current set. The CRSVM performs the k-means clustering algorithm to each class and then uses the cluster centroids to form a reduced set. The SSRSVM starts with a small initial reduced set and adds a portion of misclassified points into the reduced set iteratively based on the current classifier.

Inspired by IRSVM, we propose a feature selection algorithm, named incremental forward feature selection (IFFS). The intuition behind this method is that a new feature will be added into the current selected feature subset if it will bring in the extra information. We measure this information by using the distance between the new feature vector and the column space spanned by current feature subset. Last part of this thesis, we propose an RSVM based multi-view algorithm for semi-supervised learning, named two-teachers-one-student (2T1S). With RSVM, different from typical multi-view methods, reduced sets play distinct views in the represented kernel feature space rather than in the input space. Our 2T1S blends the concepts of co-training and consensus training. Through co-training, the classifiers generated by two views can “teach” the third classifier from the remaining view to learn, and this process is performed for each choice of teachers-student combination. By consensus training, predictions from more than one view can give us higher confidence for labeling unlabeled data.
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Applications of Reduced Set . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Notations and Problem Setting . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Preliminaries 5
2.1 A Brief Overview of the Support Vector Machines . . . . . . . . . . . . . . . . 5
2.2 Smooth Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Reduced Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Reduced Set Selection Methods 10
3.1 Incremental RSVM Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Clustering RSVM Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.3 Generating the Reduced Set by Systematic Sampling . . . . . . . . . . . . . . 13
3.3.1 Systematic Sampling for RSVM . . . . . . . . . . . . . . . . . . . . . 13
3.3.2 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4 Incremental Forward Feature Selection 17
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2 Filter Model for Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . 19
4.3 Wrapper Model for Feature Selection . . . . . . . . . . . . . . . . . . . . . . 22
4.3.1 1-norm SVM for Feature Selection . . . . . . . . . . . . . . . . . . . . 22
4.3.2 Incremental Forward Feature Selection (IFFS) . . . . . . . . . . . . . 24
4.4 Experiment Setting and Numerical Results . . . . . . . . . . . . . . . . . . . . 25
4.4.1 Acute Leukemia Data Set . . . . . . . . . . . . . . . . . . . . . . . . 25
4.4.2 Colon Cancer Data Set . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.4.3 Numerical Results and Comparisons . . . . . . . . . . . . . . . . . . . 28
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5 An RSVM Based Two-teachers-one-student Semi-supervised Learning Algorithm 33
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.2 Previous Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.3 2T1S Approach for SSL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.3.1 Reduced Sets for Multi-view Learning . . . . . . . . . . . . . . . . . . 40
5.3.2 View Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.3.3 The 2T1S algorithm = co-training + consensus training . . . . . . . . . 43
5.4 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.4.1 Five-Group Data Set . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.4.2 Checkerboard Data Set . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.4.3 UCI Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.4.4 Comparison of 2T1S with Co-training and Tri-training Algorithms . . . 53
5.5 A Variant 2T1S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.5.1 The Part-concept of Passive-Aggressive Algorithm for PASL . . . . . . 54
5.5.2 The Technique of Down-weighting for PASL . . . . . . . . . . . . . . 55
5.5.3 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.6 An Application for 2T1S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
6 Summary and Conclusion 72
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