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cluster property中文是什么意思

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  • Based on unsupervised learning , sparse coding is suitable to describe images with non - gaussian distribution and can get rid of the high order redundancy among the image pixels . since the basis function of sparse coding has build - in clustering property , it increases the inter - class variations of the features
    稀疏編碼是一種基于非監(jiān)督學習的算法,它適合描述具有非高斯分布的數據對象,能夠有效地消除圖像象素點之間的冗余,并具有內在的聚類特性。
  • Based on this kind of relations between the topological structures and the content distributions we study the web modelling , community identification and some related application problems in detail : first , after some existed characteristics of the web topology are verified , some new characteristics are discovered : the high clustering property in micro - topology ( high average gathering coefficient ) , the obvious mapping relation between the topological struture and the content in micro - level 、 linear irrelevant between the degree distribution of network nodes and the relative degree distribution of contents etc . then after analysis the topology of the complex network and the network modeling , the muti - scale determinism is proposed , especially for the information network a web evolvement model ( prcp model ) that fused the node authority and the node correlation is proposed . the model deduction , evolving learning verification and large scale experiment proof indicate that the model can explain the micro - topology centralizing phenomena , can imitate the mapping relation between the network connecting distribution and network content relative distribution and also can predict the mapping relation between the topology clustering and content clustering
    本文在詳細觀察了web網絡的拓撲結構特征以及拓撲結構與內容分布相互關系的基礎上,以信息網絡的物理連接拓撲結構與節(jié)點內容相關度分布之間的相互關系為主線,從網絡特征、網絡建模、社區(qū)分析及相關應用方面問題進行了深入細致地探討:首先在驗證了前人提出的web網絡拓撲結構特征基礎上,進一步發(fā)現了信息網絡所具有的一些新特征: 1 )網絡微觀顆粒度的拓撲結構聚團與內容聚團存在明顯的映射關系,具體包括節(jié)點之間的物理連邊概率與節(jié)點之間的內容相關度成指數比例關系、節(jié)點形成三角形拓撲結構的概率與節(jié)點內容相關緊密程度之間同樣具有一種指數比例關系; 2 )網絡節(jié)點連接度整體分布與節(jié)點內容相關度整體分布是線性無關的; 3 )網絡微觀拓撲結構中的存在很強的集聚性(平均聚團系數很高) 。
  • Based on the clustering property of the basis function of sparse coding , a basis function initialization method using fuzzy c mean algorithm is proposed to help the energy function of sparse coding to converge to a better local minimum for recognition . experimental results show that the classification and the sparseness of the features are both improved
    經過模糊c均值聚類初始化后的基函數能夠讓稀疏編碼的能量函數收斂到一個更有利于識別的局部最小點,試驗結果表明特征的分類性和稀疏性都得到了提高。
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