目的:介绍潜在类别模型的原理、方法及其分析过程,为医学模式转变所带来的病因关系的复杂性及其对统计分析方法的改进所提出的要求提供理论依据。方法:利用Mplus软件Monte Carlo simulation study模块,按照预先设定的模型产生模拟数据...目的:介绍潜在类别模型的原理、方法及其分析过程,为医学模式转变所带来的病因关系的复杂性及其对统计分析方法的改进所提出的要求提供理论依据。方法:利用Mplus软件Monte Carlo simulation study模块,按照预先设定的模型产生模拟数据并赋予一定的含义,然后导入Mplus软件直接进行潜在类别分析及多样本分析比较,用图示直观地表现模型参数变化。结果:单样本潜在类别分析显示模型M1中潜在类别2作用大于潜在类别1的作用;模型M2中潜在类别1的作用明显大于潜在类别2的作用。多样本潜在类别分析结果显示所有观察值区分为两类,模型M1与模型M2之间潜在类别具有差异性。讨论:潜在类别分析是描述一组分类变量间相互关系所形成的数学模型,综合了结构方程模型与对数线性模型的思想,可以做探索性研究,也可用于验证性研究,拓展了潜变量模型的应用范围。展开更多
We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories. The proposed feature introduces spatial information among the latent topics by means of spatial pyramid, while the l...We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories. The proposed feature introduces spatial information among the latent topics by means of spatial pyramid, while the latent topics are obtained by using probabilistic latent semantic analysis （pLSA） based on the bag-of-words representation. The proposed feature always performs better than standard pLSA because the performance of pLSA is adversely affected in many cases due to the loss of spatial information. By combining various interest point detectors and local region descriptors used in the bag-of-words model, the proposed feature can make further improvement for diverse scene category recognition tasks. We also propose a two-stage framework for multi-class classification. In the first stage, for each of possible detector/descriptor pairs, adaptive boosting classifiers are employed to select the most discriminative topics and further compute posterior probabilities of an unknown image from those selected topics. The second stage uses the prod-max rule to combine information coming from multiple sources and assigns the unknown image to the scene category with the highest ＇final＇ posterior probability. Experimental results on three benchmark scene datasets show that the proposed method exceeds most state-of-the-art methods.展开更多
文摘目的:介绍潜在类别模型的原理、方法及其分析过程,为医学模式转变所带来的病因关系的复杂性及其对统计分析方法的改进所提出的要求提供理论依据。方法:利用Mplus软件Monte Carlo simulation study模块,按照预先设定的模型产生模拟数据并赋予一定的含义,然后导入Mplus软件直接进行潜在类别分析及多样本分析比较,用图示直观地表现模型参数变化。结果:单样本潜在类别分析显示模型M1中潜在类别2作用大于潜在类别1的作用;模型M2中潜在类别1的作用明显大于潜在类别2的作用。多样本潜在类别分析结果显示所有观察值区分为两类,模型M1与模型M2之间潜在类别具有差异性。讨论:潜在类别分析是描述一组分类变量间相互关系所形成的数学模型,综合了结构方程模型与对数线性模型的思想,可以做探索性研究,也可用于验证性研究,拓展了潜变量模型的应用范围。
基金Project supported by the Fundamental Research Funds for the Central Universities,China(No.lzujbky-2013-41)the National Natural Science Foundation of China(No.61201446)the Basic Scientific Research Business Expenses of the Central University and Open Project of Key Laboratory for Magnetism and Magnetic Materials of the Ministry of Education,Lanzhou University(No.LZUMMM2015010)
文摘We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories. The proposed feature introduces spatial information among the latent topics by means of spatial pyramid, while the latent topics are obtained by using probabilistic latent semantic analysis （pLSA） based on the bag-of-words representation. The proposed feature always performs better than standard pLSA because the performance of pLSA is adversely affected in many cases due to the loss of spatial information. By combining various interest point detectors and local region descriptors used in the bag-of-words model, the proposed feature can make further improvement for diverse scene category recognition tasks. We also propose a two-stage framework for multi-class classification. In the first stage, for each of possible detector/descriptor pairs, adaptive boosting classifiers are employed to select the most discriminative topics and further compute posterior probabilities of an unknown image from those selected topics. The second stage uses the prod-max rule to combine information coming from multiple sources and assigns the unknown image to the scene category with the highest ＇final＇ posterior probability. Experimental results on three benchmark scene datasets show that the proposed method exceeds most state-of-the-art methods.
