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ファジー部分空間クラスタリングと異常値検出

     
 
Anomaly detection has several practical applications in different areas, including intrusion detection, image processing, and behavior analysis among others. Several approaches have been developed for this task such as detection by classification, nearest neighbor approach, and clustering. This paper proposes alternative clustering algorithms for the task of anomaly detection. By employing a weighted kernel extension of the least squares fitting of linear manifolds, we develop fuzzy clustering algorithms for kernel manifolds. Experimental results show that the proposed algorithms achieve promising performances compared to hard clustering techniques.
 
 

 

 
   
   
   
  Fig. 1 Normal class models. In this study, the normal class is modeled by a set of points. Classically, the anomalism of an input data point is examined with the distance to its projection onto the point set. Figure (a) describes a model that uses a single affine subspace. In (b), the normal class model is given by the union of two affine subspace. The classical distance to the set is the square Euclidean distance to the nearest subspace. In this study, we introduce the κ-distance that is the linear combination of the distances with weights κ1 and κ2,
as in (c) .
 
     
  References  
  Raissa Relator, Tsuyoshi Kato, Takuma Tomaru, Naoya Ohta, Fuzzy Multiple Subspace Fitting for Anomaly Detection, IEICE Transactions on Information & Systems, Vol.E97-D,No.10,pp.2730-2738.,Oct. 2014. [pdf][bibtex][日本語文献]  
     
     
     
 
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学生の活躍
 
共分散記述子
マハラノビス符号化
顕微鏡画像解析
平均多項式カーネル
打ち切りデータのベイズ推定
計量学習
ファジー部分空間クラスタリング
リガンド予測
酵素活性部位探索
伝達学習によるリンク予測
多タスク学習
ラベル伝播法
マイクロアレイ用カーネル
薬剤耐性予測
ネットワーク推定
カーネル推定
変分剛体変換
その他