基于局部空间变稀疏约束的多核学习方法

Local Variable Sparsity Based Multiple Kernel Learning Algorithm

局部多核学习方法根据样本所在局部空间特性选择特定的核函数组合方式,具有较好的判别能力.本文提出了一种基于局部空间变稀疏约束的多核学习方法,首先依据样本在特征空间的分布情况以软分组的方式将训练数据划分为若干数据子集.以数据子集为单位,根据在相应的局部空间内的核函数相似程度,调整核组合的稀疏程度,使用交替优化的方法进行求解.实验表明本文方法对于区分特征学习和对抗噪声方面具有的优势,因此也使得在图像场景分类问题上的准确率和稳定性得到明显提高.

Local multiple kernel learning method could learn a specific combination kernel function for various samples accordingto the local space characteristics,therefore it has better discriminant ability.In this paper,we propose a local variable sparsity based multiple kernel learning method.In our method,the samples are divided into a few groups with a soft grouping method and the sparsity of kernel weights in various local spaces isdetermined by the similarity of kernels.We use an alternative optimization method to solve this problem.The experiment on synthetic dataset indicates that ourmethod has a strong advantage in discriminative feature learning and against noise.Finally we apply our method into image scene classification and the accuracy is improved obviously.