中山大学学报（自然科学版）
中山大學學報（自然科學版）
중산대학학보（자연과학판）
ACTA SCIENTIARUM NATURALIUM UNIVERSITATIS SUNYATSENI
2009年
6期
10-17
,共8页
单类分类器%支持向量机%结构信息%二次规划%线性规划
單類分類器%支持嚮量機%結構信息%二次規劃%線性規劃
단류분류기%지지향량궤%결구신식%이차규화%선성규화
one-class classifier%support vector machine%structured information%quadratic programming%linear programming
针对现有基于超平面的单类分类器未同时考虑目标数据全局与局部信息的不足,通过在单类支持向量机One-Class SVM(OCSVM)算法中加入类内散度以反应目标数据的全局信息,提出了结构化单类支持向量机Structured OCSVM(SOCSVM),不仅使之具有全局与局部化学习的特点,同时也为诸多的SVM算法嵌入数据内在结构这类先验信息提供了统一框架.为进一步提高运算效率,在SOCSVM二次规划求解基础上,通过最小化目标数据均值到超平面的函数距离,提出了线性规划算法,同时也避免了SOCSVM必须以原点作为负类代表的不足.人工和真实数据集上的实验结果验证了嵌入目标数据结构信息的SOCSVM及其线性规划算法的有效性.
針對現有基于超平麵的單類分類器未同時攷慮目標數據全跼與跼部信息的不足,通過在單類支持嚮量機One-Class SVM(OCSVM)算法中加入類內散度以反應目標數據的全跼信息,提齣瞭結構化單類支持嚮量機Structured OCSVM(SOCSVM),不僅使之具有全跼與跼部化學習的特點,同時也為諸多的SVM算法嵌入數據內在結構這類先驗信息提供瞭統一框架.為進一步提高運算效率,在SOCSVM二次規劃求解基礎上,通過最小化目標數據均值到超平麵的函數距離,提齣瞭線性規劃算法,同時也避免瞭SOCSVM必鬚以原點作為負類代錶的不足.人工和真實數據集上的實驗結果驗證瞭嵌入目標數據結構信息的SOCSVM及其線性規劃算法的有效性.
침대현유기우초평면적단류분류기미동시고필목표수거전국여국부신식적불족,통과재단류지지향량궤One-Class SVM(OCSVM)산법중가입류내산도이반응목표수거적전국신식,제출료결구화단류지지향량궤Structured OCSVM(SOCSVM),불부사지구유전국여국부화학습적특점,동시야위제다적SVM산법감입수거내재결구저류선험신식제공료통일광가.위진일보제고운산효솔,재SOCSVM이차규화구해기출상,통과최소화목표수거균치도초평면적함수거리,제출료선성규화산법,동시야피면료SOCSVM필수이원점작위부류대표적불족.인공화진실수거집상적실험결과험증료감입목표수거결구신식적SOCSVM급기선성규화산법적유효성.
In order to distinguish the target class from outliers accurately, One-Class Classifier ( OCC) should take into account the prior knowledge of the target class. However, One-Class SVM ( OCSVM), the state-of-the-art OCC, neglects the data's distribution information while finding the optimal hyper-plane. Structured OCSVM (SOCSVM) , the novel proposed OCC, alleviates this problem by embedding the within-class scattered matrix of the target data into OCSVM. As a result, SOCSVM not only overcomes the above disadvantage of the OCSVM, but also provides a unified framework for the present SVM algorithms how to consider intrinsic structure of the data. Moreover, to improve the efficiency of SOCSVM , linear programming algorithm called SlpOCSVM is proposed to instead of the quadratic programming solving for SOCSVM. Through minimizing the functional distance of the data's mean to the hyperplane, the optimal hyperplane is attracted automatically to the place of the minimum positive half space without borrowing the origin as a representative of the outlier anymore . The experiment results on toy problem and real data sets demonstrate the advantage of SOCSVM and its linear programming algorithm.
