《計算機應用研究》|Application Research of Computers

基于LPCA的譜聚類算法

Spectral clustering algorithm based on LPCA

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作者 童濤,文國秋,譚馬龍,吳林,杜婷婷
機構 廣西師范大學 廣西多源信息挖掘與安全重點實驗室,廣西 桂林 541004
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文章編號 1001-3695(2019)11-009-3245-05
DOI 10.19734/j.issn.1001-3695.2018.04.0283
摘要 針對傳統譜聚類在構建關系矩陣時只考慮樣本的全局特征而忽略樣本的局部特征、在聚類劃分時通常需要指定聚類個數、無法對交叉點進行正確劃分等問題,提出了一種改進的基于局部主成分分析和連通圖分解的譜聚類算法。首先自動學習挑選數據集的中心點,然后使用局部主成分分析得到數據集的關系矩陣,最后用連通圖分解算法完成對關系矩陣的劃分。實驗結果表明該改進算法性能優于現有經典算法。
關鍵詞 局部主成分分析; 譜聚類; 連通圖分解; 交叉點
基金項目 國家重點研發計劃資助項目(2016YFB1000905)
國家自然科學基金資助項目(61170131,61263035,61573270,90718020)
國家“973”計劃資助項目(2013CB329404)
中國博士后科學基金資助項目(2015M570837)
廣西自然科學基金資助項目(2015GXNSFCB139011,2015GXNSFAA139306)
廣西研究生教育創新計劃項目(YCSW2019072)
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英文標題 Spectral clustering algorithm based on LPCA
作者英文名 Tong Tao, Wen Guoqiu, Tan Malong, Wu Lin, Du Tingting
機構英文名 Guangxi Key Laboratory of Multi-source Information Mining & Security,Guangxi Normal University,Guilin Guangxi 541004,China
英文摘要 As the traditional spectral clustering algorithms not only considered the global structures of the samples while ignoring their local structures for the construction of the correlation matrix, and conducted clustering with a predefined cluster number, but also could not divide the intersections correctly. This paper proposed a new method based on the local principal component analysis and the decomposition method of the connected graph. Specifically, the proposed method automatically learnt the centroids of the selected subset of the samples, obtained the correlation matrix of the samples based on the local principal component analysis, and used the decomposition method of the connected graph to partition the resulting correlation matrix. Experimental results show that the proposed algorithm performs better than the existing algorithms.
英文關鍵詞 local principal component analysis(LPCA); spectral clustering; connected graph decomposition; intersection
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收稿日期 2018/4/17
修回日期 2018/5/28
頁碼 3245-3249
中圖分類號 TP182
文獻標志碼 A
中超外援名额