In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very importan...In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views,while fusing these data. Multi-view Clustering(MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets.Overall, this paper serves as an introductory text and survey for multi-view clustering.展开更多
针对多模态情感分析中的模态内部特征表示和模态间的特征融合问题,结合注意力机制和多任务学习,提出了一种基于注意力的多层次混合融合的多任务多模态情感分析模型MAM(multi-level attention and multi-task)。首先,利用卷积神经网络和...针对多模态情感分析中的模态内部特征表示和模态间的特征融合问题,结合注意力机制和多任务学习,提出了一种基于注意力的多层次混合融合的多任务多模态情感分析模型MAM(multi-level attention and multi-task)。首先,利用卷积神经网络和双向门控循环单元来实现单模态内部特征的提取;其次,利用跨模态注意力机制实现模态间的两两特征融合;再次,在不同层次使用自注意力机制实现模态贡献度选择;最后,结合多任务学习获得情感和情绪的分类结果。在公开的CMU-MOSEI数据集上的实验结果表明,情感和情绪分类的准确率和F;值均有所提升。展开更多
语音情感识别广泛应用于车载驾驶系统、服务行业、教育以及医疗等各个领域。为了使计算机能更准确地识别出说话人的情感,提出了一种基于注意力机制的多任务三维卷积神经网络(ConvolutionNeuralNetwork,CNN)和双向长短期记忆网络(Bidirec...语音情感识别广泛应用于车载驾驶系统、服务行业、教育以及医疗等各个领域。为了使计算机能更准确地识别出说话人的情感,提出了一种基于注意力机制的多任务三维卷积神经网络(ConvolutionNeuralNetwork,CNN)和双向长短期记忆网络(BidirectionalLong-Short Term Memory,BLSTM)相结合的情感语音识别方法(3D CNN-BLSTM)。基于多谱特征融合组图,利用三维卷积神经网络提取深层语音情感特征,结合性别分类的多任务学习机制提升语音情感识别准确率。在CASIA汉语情感语料库上的实验结果表明,该方法获得了较高的准确率。展开更多
基金supported in part by the National Natural Science Foundation of China (No. 61572407)
文摘In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views,while fusing these data. Multi-view Clustering(MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets.Overall, this paper serves as an introductory text and survey for multi-view clustering.
文摘针对多模态情感分析中的模态内部特征表示和模态间的特征融合问题,结合注意力机制和多任务学习,提出了一种基于注意力的多层次混合融合的多任务多模态情感分析模型MAM(multi-level attention and multi-task)。首先,利用卷积神经网络和双向门控循环单元来实现单模态内部特征的提取;其次,利用跨模态注意力机制实现模态间的两两特征融合;再次,在不同层次使用自注意力机制实现模态贡献度选择;最后,结合多任务学习获得情感和情绪的分类结果。在公开的CMU-MOSEI数据集上的实验结果表明,情感和情绪分类的准确率和F;值均有所提升。
文摘语音情感识别广泛应用于车载驾驶系统、服务行业、教育以及医疗等各个领域。为了使计算机能更准确地识别出说话人的情感,提出了一种基于注意力机制的多任务三维卷积神经网络(ConvolutionNeuralNetwork,CNN)和双向长短期记忆网络(BidirectionalLong-Short Term Memory,BLSTM)相结合的情感语音识别方法(3D CNN-BLSTM)。基于多谱特征融合组图,利用三维卷积神经网络提取深层语音情感特征,结合性别分类的多任务学习机制提升语音情感识别准确率。在CASIA汉语情感语料库上的实验结果表明,该方法获得了较高的准确率。
