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To address these challenges, we propose a novel transfer multiple kernel learning method for fusing multilevel features (MFF\u2010TMKL), based on two primary constraints. The first constraint focuses on the fusion of emotional information across multilevel features. To achieve this, we introduce a weighted collaborative representation constraint designed to capture both the similarity and distinctiveness of multilevel features within a space induced by multiple kernel functions. The second constraint aims to minimize the maximum mean discrepancy (MMD) of multilevel features within this same kernel\u2010induced space. 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