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MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm

机译:MultiK-MHKS:一种新颖的多核学习算法

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With the newly-proposed Canonical Correlation Analysis (CCA) named NmCCA that is an alternative formulation of CCA for more than two views of the same phenomenon, we develop a new effective multiple kernel learning algorithm. First, we adopt the empirical kernels to map the input data into m different feature spaces corresponding to different kernels. Then through the incorporation of NmCCA in a learning algorithm, one single learning process based on the regularization learning is developed, where a special term called Inter-Function Similarity Loss RIFSL is introduced for the agreement of multi-view outputs. In implementation, we select the Modification of Ho-Kashyap algorithm with Squared approximation of the misclassification errors (MHKS) as the incorporated paradigm, and the experimental results on benchmark datasets demonstrate the feasibility and effectiveness of the proposed algorithm named MultiK-MHKS.
机译:使用新提议的规范相关分析(CCA)命名为NmCCA,它是针对相同现象的两个以上视图的CCA的替代表达,我们开发了一种新的有效的多核学习算法。首先,我们采用经验核将输入数据映射到对应于不同核的m个不同特征空间中。然后,通过将NmCCA纳入学习算法中,开发了一个基于正则化学习的单一学习过程,其中引入了一个称为功能间相似性损失RIFSL的特殊术语来表示多视图输出的一致性。在实现中,我们选择了将Ho-Kashyap算法的修正与误分类误差(MHKS)的平方近似作为合并范式,并且在基准数据集上的实验结果证明了所提出的算法MultiK-MHKS的可行性和有效性。

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