ASLSL：基于不完整多模态生理数据的自适应共享潜在结构学习用于多维情感特征选择
ASLSL: Adaptive shared latent structure learning with incomplete multi-modal physiological data for multi-dimensional emotional feature selection
AI总结：
针对多模态生理数据不完整及特征高维冗余导致情感分类性能下降的问题，提出自适应共享潜在结构学习方法（ASLSL），通过探索共享潜在空间挖掘共识信息并减轻缺失影响，在DEAP和DREAMER数据集上验证了其优于17种特征选择方法的有效性。
摘要：
近期，基于多模态生理信号的情感识别在脑机接口领域受到越来越多的关注。然而，相关的多模态生理特征通常具有高维特性，且不可避免地包含无关、冗余和有噪声的表示，这很容易导致情感分类器出现过拟合、性能差和高计算复杂度。特征选择已被广泛应用于应对这些挑战。然而，以往的研究通常假设多模态生理数据是完整的，但在现实中，由于采集和操作环境的开放性，数据往往是不完整的。例如，部分样本在几种模态中可用，但在其他模态中不可用。为了解决这个问题，我们提出了一种用于不完整多模态生理信号特征选择的新方法，称为自适应共享潜在结构学习（ASLSL）。基于相似特征共享相似情感标签的特性，ASLSL采用自适应共享潜在结构学习，探索不完整多模态生理信号与多维情感标签共享的公共潜在空间，从而减轻缺失信息的影响并挖掘共识信息。研究利用了两个最流行的带有多维情感标签的多模态生理情感数据集（DEAP和DREAMER），比较了ASLSL与十七种特征选择方法的性能。在这些数据集上的综合实验结果证明了ASLSL的有效性。
英文摘要：
Recently, multi-modal physiological signals based emotion recognition has garnered increasing attention in the field of brain-computer interfaces. Nevertheness, the associated multi-modal physiological features are often high-dimensional and inevitably include irrelevant, redundant, and noisy representation, which can easily lead to overfitting, poor performance, and high computational complexity in emotion classifiers. Feature selection has been widely applied to address these challenges. However, previous studies generally assumed that multi-modal physiological data are complete, whereas in reality, the data are often incomplete due to the openness of the acquisition and operational environment. For example, a part of samples are available in several modalities but not in others. To address this issue, we propose a novel method for incomplete multi-modal physiological signal feature selection called adaptive shared latent structure learning (ASLSL). Based on the property that similar features share similar emotional labels, ASLSL employs adaptive shared latent structure learning to explore a common latent space shared for incomplete multi-modal physiological signals and multi-dimensional emotional labels, thereby mitigating the impact of missing information and mining consensus information. Two most popular multi-modal physiological emotion datasets (DEAP and DREAMER) with multi-dimensional emotional labels were utilized to compare the performance between compare ASLSL and seventeen feature selection methods. Comprehensive experimental results on these datasets demonstrate the effectiveness of ASLSL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
