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41.发明授权Social network analysis with prior knowledge and non-negative tensor factorization 有权社会网络分析与先验知识和非负张量分解公开(公告)号：US08346708B2
公开(公告)日：2013-01-01
申请号：US12469043
申请日：2009-05-20
申请人： Yun Chi , Shenghuo Zhu , Koji Hino , Yihong Gong
发明人： Yun Chi , Shenghuo Zhu , Koji Hino , Yihong Gong
CPC分类号： G06Q30/02
摘要： Systems and methods are disclosed to analyze a social network by generating a data tensor from social networking data; applying a non-negative tensor factorization (NTF) with user prior knowledge and preferences to generate a core tensor and facet matrices; and rendering information to social networking users based on the core tensor and facet matrices.
摘要翻译： 公开了通过从社交网络数据生成数据张量来分析社交网络的系统和方法; 应用具有用户先验知识和偏好的非负张量因子分解（NTF）来生成核心张量和小平面矩阵; 并基于核心张量和面矩阵将信息呈现给社交网络用户。
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42.发明授权公开(公告)号：US08131732B2
公开(公告)日：2012-03-06
申请号：US12331346
申请日：2008-12-09
申请人： Kai Yu , Shenghuo Zhu , Yihong Gong
发明人： Kai Yu , Shenghuo Zhu , Yihong Gong
CPC分类号： G06F17/30867 , G06F17/16
摘要： A system is disclosed with a collaborative filtering engine to predict an active user's ratings/interests/preferences on a set of new products/items. The predictions are based on an analysis the database containing the historical data of many users' ratings/interests/preferences on a large set of products/items.
摘要翻译： 公开了一种具有协作过滤引擎的系统，以预测一组新产品/项目上的主动用户的评级/兴趣/偏好。 这些预测是基于对包含大量产品/项目的许多用户评级/兴趣/偏好的历史数据的数据库的分析。
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公开(公告)号：US20100124377A1
公开(公告)日：2010-05-20
申请号：US12469073
申请日：2009-05-20
申请人： Kai Yu , Jianchao Yang , Yihong Gong
发明人： Kai Yu , Jianchao Yang , Yihong Gong
IPC分类号： G06K9/62
CPC分类号： G06K9/6249 , G06K9/4671
摘要： Systems and methods are disclosed to classify an input image by determining a spatial-pyramid image representation based on sparse coding; determining a descriptor for each interest point in the input image; encoding the descriptor; and applying max pooling to form the spatial pyramid representation of images.
摘要翻译： 公开了系统和方法，以通过基于稀疏编码确定空间 - 金字塔图像表示来对输入图像进行分类; 确定所述输入图像中每个兴趣点的描述符; 编码描述符; 并应用最大池来形成图像的空间金字塔表示。
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44.发明授权Method and system for segmentation, classification, and summarization of video images 有权视频图像的分割，分类和总结方法和系统公开(公告)号：US07630562B2
公开(公告)日：2009-12-08
申请号：US11505833
申请日：2006-08-18
申请人： Yihong Gong , Xin Liu
发明人： Yihong Gong , Xin Liu
CPC分类号： G06K9/00711 , G06F17/30802 , G06F17/30843 , G11B27/031 , H04N5/147 , Y10S707/99933
摘要： In a technique for video segmentation, classification and summarization based on the singular value decomposition, frames of the input video sequence are represented by vectors composed of concatenated histograms descriptive of the spatial distributions of colors within the video frames. The singular value decomposition maps these vectors into a refined feature space. In the refined feature space produced by the singular value decomposition, the invention uses a metric to measure the amount of information contained in each video shot of the input video sequence. The most static video shot is defined as an information unit, and the content value computed from this shot is used as a threshold to cluster the remaining frames. The clustered frames are displayed using a set of static keyframes or a summary video sequence. The video segmentation technique relies on the distance between the frames in the refined feature space to calculate the similarity between frames in the input video sequence. The input video sequence is segmented based on the values of the calculated similarities. Finally, average video attribute values in each segment are used in classifying the segments.
摘要翻译： 在基于奇异值分解的视频分割，分类和聚合的技术中，输入视频序列的帧由描述视频帧内的颜色的空间分布的级联直方图组成的矢量表示。 奇异值分解将这些向量映射到精细的特征空间。 在通过奇异值分解产生的精细特征空间中，本发明使用度量来测量输入视频序列的每个视频镜头中包含的信息量。 最静态的视频镜头被定义为信息单元，并且从该镜头计算的内容值被用作将剩余帧聚类的阈值。 使用一组静态关键帧或摘要视频序列显示群集帧。 视频分割技术依赖于精细特征空间中的帧之间的距离来计算输入视频序列中的帧之间的相似度。 输入视频序列基于所计算的相似度的值进行分段。 最后，在分段中使用每个段中的平均视频属性值。
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公开(公告)号：US20080147852A1
公开(公告)日：2008-06-19
申请号：US11869892
申请日：2007-10-10
申请人： Shenghuo Zhu , Yihong Gong
发明人： Shenghuo Zhu , Yihong Gong
IPC分类号： G06F15/173
CPC分类号： H04L41/5061 , H04L41/5074
摘要： Systems and methods are disclosed that performs active feature probing using data augmentation. Active feature probing is a means of actively gathering information when the existing information is inadequate for decision making. The data augmentation technique generates factitious data which complete the existing information. Using the factitious data, the system is able to estimate the reliability of classification, and determine the most informative feature to probe, then gathers the additional information. The features are sequentially probed until the system has adequate information to make the decision.
摘要翻译： 公开了使用数据增加来执行主动特征探测的系统和方法。 主动特征探测是当现有信息不足以决策时积极收集信息的一种手段。 数据增加技术生成完成现有信息的数据。 使用事实数据，系统能够估计分类的可靠性，并确定最具信息性的特征进行探测，然后收集附加信息。 这些功能被依次探测，直到系统有足够的信息作出决定。
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公开(公告)号：US20060280365A1
公开(公告)日：2006-12-14
申请号：US11505833
申请日：2006-08-18
申请人： Yihong Gong , Xin Liu
发明人： Yihong Gong , Xin Liu
CPC分类号： G06K9/00711 , G06F17/30802 , G06F17/30843 , G11B27/031 , H04N5/147 , Y10S707/99933
摘要： In a technique for video segmentation, classification and summarization based on the singular value decomposition, frames of the input video sequence are represented by vectors composed of concatenated histograms descriptive of the spatial distributions of colors within the video frames. The singular value decomposition maps these vectors into a refined feature space. In the refined feature space produced by the singular value decomposition, the invention uses a metric to measure the amount of information contained in each video shot of the input video sequence. The most static video shot is defined as an information unit, and the content value computed from this shot is used as a threshold to cluster the remaining frames. The clustered frames are displayed using a set of static keyframes or a summary video sequence. The video segmentation technique relies on the distance between the frames in the refined feature space to calculate the similarity between frames in the input video sequence. The input video sequence is segmented based on the values of the calculated similarities. Finally, average video attribute values in each segment are used in classifying the segments.
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公开(公告)号：US08402369B2
公开(公告)日：2013-03-19
申请号：US12199104
申请日：2008-08-27
申请人： Shenghuo Zhu , Dingding Wang , Yun Chi , Yihong Gong
发明人： Shenghuo Zhu , Dingding Wang , Yun Chi , Yihong Gong
IPC分类号： G06F17/00
CPC分类号： G06F17/30719
摘要： Systems and methods are disclosed for summarizing multiple documents by generating a model of the documents as a mixture of document clusters, each document in turn having a mixture of sentences, wherein the model simultaneously representing summarization information and document cluster structure; and determining a loss function for evaluating the model and optimizing the model.
摘要翻译： 公开了系统和方法，用于通过将文档的模型作为文档集合的混合来生成多个文档，每个文档又具有句子的混合，其中模型同时表示摘要信息和文档集群结构; 并确定用于评估模型和优化模型的损失函数。
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公开(公告)号：US08374442B2
公开(公告)日：2013-02-12
申请号：US12469073
申请日：2009-05-20
申请人： Kai Yu , Jianchao Yang , Yihong Gong
发明人： Kai Yu , Jianchao Yang , Yihong Gong
CPC分类号： G06K9/6249 , G06K9/4671
摘要： Systems and methods are disclosed to classify an input image by determining a spatial-pyramid image representation based on sparse coding; determining a descriptor for each interest point in the input image; encoding the descriptor; and applying max pooling to form the spatial pyramid representation of images.
摘要翻译： 公开了系统和方法，以通过基于稀疏编码确定空间 - 金字塔图像表示来对输入图像进行分类; 确定所述输入图像中每个兴趣点的描述符; 编码描述符; 并应用最大池来形成图像的空间金字塔表示。
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公开(公告)号：US08090665B2
公开(公告)日：2012-01-03
申请号：US12277305
申请日：2008-11-25
申请人： Tianbao Yang , Shenghuo Zhu , Yun Chi , Yihong Gong
发明人： Tianbao Yang , Shenghuo Zhu , Yun Chi , Yihong Gong
IPC分类号： G06Q99/00
摘要： Systems and methods are disclosed to find dynamic social networks by applying a dynamic stochastic block model to generate one or more dynamic social networks, wherein the model simultaneously captures communities and their evolutions, and inferring best-fit parameters for the dynamic stochastic model with online learning and offline learning.
摘要翻译： 公开了系统和方法以通过应用动态随机块模型来生成一个或多个动态社交网络来发现动态社交网络，其中模型同时捕获社区及其演化，并且通过在线学习推导动态随机模型的最佳拟合参数 和离线学习。
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公开(公告)号：US20100076913A1
公开(公告)日：2010-03-25
申请号：US12277305
申请日：2008-11-25
申请人： Tianbao Yang , Yun Chi , Shenghuo Zhu , Yihong Gong
发明人： Tianbao Yang , Yun Chi , Shenghuo Zhu , Yihong Gong
摘要： Systems and methods are disclosed to find dynamic social networks by applying a dynamic stochastic block model to generate one or more dynamic social networks, wherein the model simultaneously captures communities and their evolutions, and inferring best-fit parameters for the dynamic stochastic model with online learning and offline learning.
摘要翻译： 公开了系统和方法以通过应用动态随机块模型来生成一个或多个动态社交网络来发现动态社交网络，其中模型同时捕获社区及其演化，并且通过在线学习推导动态随机模型的最佳拟合参数 和离线学习。
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