计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2010年
5期
57-58,61
,共3页
超完备字典%稀疏分解%稀疏映射%重构误差
超完備字典%稀疏分解%稀疏映射%重構誤差
초완비자전%희소분해%희소영사%중구오차
overcomplete dictionary%sparse decomposition%sparse mapping%reconstruction error
利用基于超完备字典的信号稀疏分解理论,提出一种基于稀疏分解的数据分类算法SRC.该算法通过学习不同类别数据的稀疏映射关系,把测试样本映射到高维空间中,根据稀疏重构的误差定义决策函数以确定测试样本的类别.采用UCI数据集评估该算法,并与SVM算法和Fld算法的实验结果进行对比,结果表明,SRC的分类准确率最高,不平衡数据集的实验结果显示了SRC的鲁棒性.
利用基于超完備字典的信號稀疏分解理論,提齣一種基于稀疏分解的數據分類算法SRC.該算法通過學習不同類彆數據的稀疏映射關繫,把測試樣本映射到高維空間中,根據稀疏重構的誤差定義決策函數以確定測試樣本的類彆.採用UCI數據集評估該算法,併與SVM算法和Fld算法的實驗結果進行對比,結果錶明,SRC的分類準確率最高,不平衡數據集的實驗結果顯示瞭SRC的魯棒性.
이용기우초완비자전적신호희소분해이론,제출일충기우희소분해적수거분류산법SRC.해산법통과학습불동유별수거적희소영사관계,파측시양본영사도고유공간중,근거희소중구적오차정의결책함수이학정측시양본적유별.채용UCI수거집평고해산법,병여SVM산법화Fld산법적실험결과진행대비,결과표명,SRC적분류준학솔최고,불평형수거집적실험결과현시료SRC적로봉성.
With the theory of sparse decomposition of signals over an overcomplete dictionary,this paper proposes a data classification algorithm based on sparse decomposition named SRC.By studying data sparse mapping relationships among different data classes,the test samples are mapped into a higher dimensional space.Decision function is defined according to the crror of sparse reconstruction,which determines the class of test samples.It uses UCI dataset to evaluate the effectiveness of the algorithm,and compares the experimental results of Support Vector Machine(SVM)and Fld.The results show that SRC gains the highest accuracy in classification,and it has good robustness in the imbalanced dataset experiment.
