It is essential to utilize deep-learning algorithms based on big data for the implementation of the new generation of artificial intelligence. Effective utilization of deep learning relies considerably on the number o...It is essential to utilize deep-learning algorithms based on big data for the implementation of the new generation of artificial intelligence. Effective utilization of deep learning relies considerably on the number of labeled samples, which restricts the application of deep learning in an environment with a small sample size. In this paper, we propose an approach based on a generative adversarial network (GAN) combined with a deep neural network (DNN). First, the original samples were divided into a training set and a test set. The GAN was trained with the training set to generate synthetic sample data, which enlarged the training set. Next, the DNN classifier was trained with the synthetic samples. Finally, the classifier was tested with the test set, and the effectiveness of the approach for multi-classification with a small sample size was validated by the indicators. As an empirical case, the approach was then applied to identify the stages of cancers with a small labeled sample size. The experimental results verified that the proposed approach achieved a greater accuracy than traditional methods. This research was an attempt to transform the classical statistical machine-learning classification method based on original samples into a deep-learning classification method based on data augmentation. The use of this approach will contribute to an expansion of application scenarios for the new generation of artificial intelligence based on deep learning, and to an increase in application effectiveness. This research is also expected to contribute to the comprehensive promotion of new-generation artificial intelligence.展开更多
Low-dimensional feature representation with enhanced discriminatory power of paramount importance to face recognition systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disad...Low-dimensional feature representation with enhanced discriminatory power of paramount importance to face recognition systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the “small sample size” (SSS) problem which is often encountered in face recognition tasks. In this paper, we propose a new technique coined Relevance-Weighted Two Dimensional Linear Discriminant Analysis (RW2DLDA). Its over comes the singularity problem implicitly, while achieving efficiency. Moreover, a weight discriminant hyper plane is used in the between class scatter matrix, and RW method is used in the within class scatter matrix to weigh the information to resolve confusable data in these classes. Experiments on two well known facial databases show the effectiveness of the proposed method. Comparisons with other LDA-based methods show that our method improves the LDA classification performance.展开更多
目的当竞争风险存在时,基于限制平均损失时间(restricted mean time lost,RMTL)的方法具有较少的模型假设条件和更直观的解释性。组间效应量为RMTL差值(RMTL difference,RMTLd),对应假设检验基于大样本下构建,而在小样本假设下的表现效...目的当竞争风险存在时,基于限制平均损失时间(restricted mean time lost,RMTL)的方法具有较少的模型假设条件和更直观的解释性。组间效应量为RMTL差值(RMTL difference,RMTLd),对应假设检验基于大样本下构建,而在小样本假设下的表现效果未知。方法本文探讨RMTLd在小样本下的表现,并发展了几种RMTL的变量转换法以提高此时的统计性能,且通过Monte Carlo模拟评价它们在不同情形下的Ⅰ类错误和检验效能。结果在小样本下,RMTLd检验原方法存在Ⅰ类错误膨胀的现象,而四种转换法之一的逻辑转换法能够保持较好的统计性能。结论在分析小样本竞争风险数据时,推荐使用RMTL的逻辑转换进行统计分析。展开更多
高光谱成像技术在实现低成本大范围的土壤重金属快速监测方面独具潜力。针对高光谱图像反演中突出的小样本问题,本文基于分数阶微分FOD(Fractional Order Derivative)提出一种面向高光谱图像的土壤重金属反演方法。首先,利用土壤采样点...高光谱成像技术在实现低成本大范围的土壤重金属快速监测方面独具潜力。针对高光谱图像反演中突出的小样本问题,本文基于分数阶微分FOD(Fractional Order Derivative)提出一种面向高光谱图像的土壤重金属反演方法。首先,利用土壤采样点的邻近像元进行样本扩充,增加样本的光谱差异性;其次,采用FOD突出光谱特征同时保留微分光谱的渐变信息;进而通过竞争自适应重加权采样CARS(Competitive Adaptive Reweighted Sampling)优选波段,采用偏最小二乘方法(PLSR)建立反演模型。以新疆维吾尔自治区哈密市黄山南矿区获取的72个土壤样本和航空高光谱图像为研究数据,对铅(Pb)、锌(Zn)、镍(Ni)3种重金属进行反演,结果表明:样本扩充不仅缓和了模型的过拟合现象,还提升了重金属反演精度;最佳阶数的分数阶微分能有效增强光谱特征,提高反演精度;CARS相对于相关系数法CC(Correlation Coefficient)、遗传算法GA(Genetic Algorithm)选出的波段组合反演精度更优,对研究区重金属Pb、Zn、Ni的反演精度R2分别为0.7974、0.8690和0.8303,反演方法具有较好的鲁棒性。展开更多
基金the National Natural Science Foundation of China (91646102, L1724034, L16240452, L1524015, and 20905027)the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (16JDGC011)+3 种基金the Chinese Academy of Engineering’s China Knowledge Center for Engineering Sciences and Technology Project (CKCEST-2018-1-13)the UK– China Industry Academia Partnership Programme (UK-CIAPP/260)Volvo-Supported Green Economy and Sustainable Development at Tsinghua University (20153000181)the Tsinghua Initiative Research Project (2016THZW).
文摘It is essential to utilize deep-learning algorithms based on big data for the implementation of the new generation of artificial intelligence. Effective utilization of deep learning relies considerably on the number of labeled samples, which restricts the application of deep learning in an environment with a small sample size. In this paper, we propose an approach based on a generative adversarial network (GAN) combined with a deep neural network (DNN). First, the original samples were divided into a training set and a test set. The GAN was trained with the training set to generate synthetic sample data, which enlarged the training set. Next, the DNN classifier was trained with the synthetic samples. Finally, the classifier was tested with the test set, and the effectiveness of the approach for multi-classification with a small sample size was validated by the indicators. As an empirical case, the approach was then applied to identify the stages of cancers with a small labeled sample size. The experimental results verified that the proposed approach achieved a greater accuracy than traditional methods. This research was an attempt to transform the classical statistical machine-learning classification method based on original samples into a deep-learning classification method based on data augmentation. The use of this approach will contribute to an expansion of application scenarios for the new generation of artificial intelligence based on deep learning, and to an increase in application effectiveness. This research is also expected to contribute to the comprehensive promotion of new-generation artificial intelligence.
文摘Low-dimensional feature representation with enhanced discriminatory power of paramount importance to face recognition systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the “small sample size” (SSS) problem which is often encountered in face recognition tasks. In this paper, we propose a new technique coined Relevance-Weighted Two Dimensional Linear Discriminant Analysis (RW2DLDA). Its over comes the singularity problem implicitly, while achieving efficiency. Moreover, a weight discriminant hyper plane is used in the between class scatter matrix, and RW method is used in the within class scatter matrix to weigh the information to resolve confusable data in these classes. Experiments on two well known facial databases show the effectiveness of the proposed method. Comparisons with other LDA-based methods show that our method improves the LDA classification performance.
文摘目的当竞争风险存在时,基于限制平均损失时间(restricted mean time lost,RMTL)的方法具有较少的模型假设条件和更直观的解释性。组间效应量为RMTL差值(RMTL difference,RMTLd),对应假设检验基于大样本下构建,而在小样本假设下的表现效果未知。方法本文探讨RMTLd在小样本下的表现,并发展了几种RMTL的变量转换法以提高此时的统计性能,且通过Monte Carlo模拟评价它们在不同情形下的Ⅰ类错误和检验效能。结果在小样本下,RMTLd检验原方法存在Ⅰ类错误膨胀的现象,而四种转换法之一的逻辑转换法能够保持较好的统计性能。结论在分析小样本竞争风险数据时,推荐使用RMTL的逻辑转换进行统计分析。
