Description: lsa（潜在语义分析的论文）一个关于概率模型的论文-lsa (Latent Semantic Analysis of the papers) a paper on the probabilistic model Platform: |
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Author:祝津津 |
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Description: This paperpresent a novel framework for inferring global behaviour patterns through modelling behaviour correlations in a wide-area scene and detecting any
anomaly in behaviours occurring both locally and globally. Specifically,
This paperpropose a semantic scene segmentation model to decompose a wide-area
scene into regions where behaviours share similar characteristic and are represented as classes of video events bearing similar features. To model behavioural correlations globally, This paperinvestigate both a probabilistic Latent Semantic Analysis (pLSA) model and a two-stage hierarchical pLSA model for
global behaviour inference and anomaly detection. The proposed framework
is validated by experiments using complex crowded outdoor scenes. Platform: |
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Author:shpark |
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Description: Probabilistic Latent Semantic Analysis ... using a number of other programming languages such as C++ and Java etc Platform: |
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Author:Bharat Singh |
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Description: 这是一篇综述文章，介绍文本分析的参数估计问题，主要是贝叶斯模型包括PLSA，LDA等-This technical note is intended to review the foundations of Bayesian parameter estimation in the discrete domain, which is necessary to understand the inner workings of
topic-based text analysis approaches like probabilistic latent semantic analysis (PLSA), latent Dirichlet allocation (LDA) and other mixture models of count data. Platform: |
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Author:luyao |
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Description: 种基于期望最大化( E M) 算法的局部图像特征的语义提取方法。首先提取图像的局部图像特 征， 统计特征在视觉词汇本中的出现频率， 将图像表示成词袋模型； 引入文本分析中的潜在语义分析技术建立从低层图像 特征到高层图像语义之间的映射模型； 然后利用 E M 算法拟合概率模型， 得到图像局部特征的潜在语义概率分布； 最后利 用该模型提取出的图像在潜在语义上的分布来进行图像分析和理解。-Semantic extraction of local image features based on expectation maximization (E M) algorithm. First extract the local features of the image, the visual vocabulary in the frequency of statistical feature, the image into the bag of words model introduce the latent semantic analysis of the text the technology to establish the mapping model between image low-level features to high-level semantic image and then use the E M algorithm for fitting probability model, probabilistic latent semantic distribution of local image features the distribution of the final image by using the model extracted in the latent semantic of image analysis and understanding. Platform: |
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Author:杨雪 |
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Search - probabilistic latent semantic analysis