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    • 5. 发明授权
    • Scalable probabilistic latent semantic analysis
    • 可扩展概率潜在语义分析
    • US07844449B2
    • 2010-11-30
    • US11392763
    • 2006-03-30
    • Chenxi LinJie HanGuirong XueHua-Jun ZengBenyu ZhangZheng ChenJian Wang
    • Chenxi LinJie HanGuirong XueHua-Jun ZengBenyu ZhangZheng ChenJian Wang
    • G06F17/27
    • G06F17/2785
    • A scalable two-pass scalable probabilistic latent semantic analysis (PLSA) methodology is disclosed that may perform more efficiently, and in some cases more accurately, than traditional PLSA, especially where large and/or sparse data sets are provided for analysis. The improved methodology can greatly reduce the storage and/or computational costs of training a PLSA model. In the first pass of the two-pass methodology, objects are clustered into groups, and PLSA is performed on the groups instead of the original individual objects. In the second pass, the conditional probability of a latent class, given an object, is obtained. This may be done by extending the training results of the first pass. During the second pass, the most likely latent classes for each object are identified.
    • 公开了一种可扩展的双向可伸缩概率潜在语义分析(PLSA)方法,其可以比传统的PLSA更有效地执行,在某些情况下可以更准确地执行,特别是在提供大型和/或稀疏数据集用于分析的情况下。 改进的方法可以大大降低培训PLSA模型的存储和/或计算成本。 在双路方法的第一遍中,对象被聚集成组,并且PLSA在组而不是原始的单个对象上执行。 在第二遍中,获得给定对象的潜在类的条件概率。 这可以通过扩展第一遍的训练结果来完成。 在第二遍期间,识别每个对象最可能的潜在类。
    • 8. 发明申请
    • Scalable probabilistic latent semantic analysis
    • 可扩展概率潜在语义分析
    • US20070239431A1
    • 2007-10-11
    • US11392763
    • 2006-03-30
    • Chenxi LinJie HanGuirong XueHua-Jun ZengBenyu ZhangZheng ChenJian Wang
    • Chenxi LinJie HanGuirong XueHua-Jun ZengBenyu ZhangZheng ChenJian Wang
    • G06F17/27
    • G06F17/2785
    • A scalable two-pass scalable probabilistic latent semantic analysis (PLSA) methodology is disclosed that may perform more efficiently, and in some cases more accurately, than traditional PLSA, especially where large and/or sparse data sets are provided for analysis. The improved methodology can greatly reduce the storage and/or computational costs of training a PLSA model. In the first pass of the two-pass methodology, objects are clustered into groups, and PLSA is performed on the groups instead of the original individual objects. In the second pass, the conditional probability of a latent class, given an object, is obtained. This may be done by extending the training results of the first pass. During the second pass, the most likely latent classes for each object are identified.
    • 公开了一种可扩展的双向可伸缩概率潜在语义分析(PLSA)方法,其可以比传统的PLSA更有效地执行,在某些情况下可以更准确地执行,特别是在提供大数据集和/或稀疏数据集用于分析的情况下。 改进的方法可以大大降低培训PLSA模型的存储和/或计算成本。 在双路方法的第一遍中,对象被聚集成组,并且PLSA在组而不是原始的单个对象上执行。 在第二遍中,获得给定对象的潜在类的条件概率。 这可以通过扩展第一遍的训练结果来完成。 在第二遍期间,识别每个对象最可能的潜在类。