CAJ | 학술논문

核匹配追踪算法在生成函数字典的过程中常采用贪婪算法进行全局最优搜索，导致算法学习时间过长。该文针对这一缺陷，提出一种基于近邻传播(Affinity Propagation, AP)聚类与核匹配追踪相结合的分类方法(AP-Kernel Matching Pursuit, AP-KMP)，该方法利用聚类算法来优化核匹配追踪算法中的字典划分过程，使用近邻传播聚类将目标数据集划分为若干小型字典空间，随后KMP算法在小型字典空间进行局部搜索，从而缩短学习时间。针对部分UCI数据集和遥感图像数据集，分别采用AP-KMP算法与另4种经典算法进行分类比较实验，结果表明该文算法在时间开销和分类性能上均有一定的优越性。
핵필배추종산법재생성함수자전적과정중상채용탐람산법진행전국최우수색，도치산법학습시간과장。해문침대저일결함，제출일충기우근린전파(Affinity Propagation, AP)취류여핵필배추종상결합적분류방법(AP-Kernel Matching Pursuit, AP-KMP)，해방법이용취류산법래우화핵필배추종산법중적자전화분과정，사용근린전파취류장목표수거집화분위약간소형자전공간，수후KMP산법재소형자전공간진행국부수색，종이축단학습시간。침대부분UCI수거집화요감도상수거집，분별채용AP-KMP산법여령4충경전산법진행분류비교실험，결과표명해문산법재시간개소화분류성능상균유일정적우월성。
The processing of generating dictionary of function in Kernel Matching Pursuit (KMP) often uses greedy algorithm for global optimal searching, the dictionary learning time of KMP is too long. To overcome the above drawbacks, a novel classification algorithm (AP-KMP) based on Affinity Propagation (AP) and KMP is proposed. This method utilizes clustering algorithms to optimize dictionary division process in KMP algorithm, then the KMP algorithm is used to search in these local dictionary space, thus reducing the computation time. Finally, four algorithms and AP-KMP are carried out respectively for some UCI datasets and remote sensing image datasets, the conclusion of which fully demonstrates that the AP-KMP algorithm is superior over another four algorithms in computation time and classification performance.