This package contains code for "Noisy Label Detection" with Spannogram and Sequential Linearization Program. 
It contains following algortihms 

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function [Yall,P,spT]=LND_spanno(K,kList,id_expert,Y,nd)
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 Label Noise Detection method with spannogram framework with level of approximation "nd"(for current version nd<=3)
 
Input :  K = Kernel matrix 
          KList = List of numbers of noisy points 
          kList(i,:) = Denote i^th try of various noisy ratio 
          kList(i,1) = Number of positively annotated datapoint is noisy.
          kList(i,2) = Number of negatively annotated datapoint is noisy.
          id_expert = List of index corresponding to expert annotation.
          Y = Annotation
          nd= Level of approximation
 Output : Yall = Matrix of corrected labels. 
          Yall(:,i)=  Corrected labels considering number of noisy data points in kList(i,:)
          spT = time spent for noise detection

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 function [Yall,spT]=LND_SLP(K,kList,id_expert,Y)
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 Label Noise Detection method with Linear Sequentialization Algorithm  with random intial points.


 Input :  K = Kernel matrix 
          KList = List of numbers of noisy points 
          kList(i,:) = Denote i^th try of various noisy ratio 
          kList(i,1) = Number of positively annotated datapoint is noisy.
          kList(i,2) = Number of negatively annotated datapoint is noisy.
          id_expert = List of index corresponding to expert annotation.
          Y = Annotation
          Num= Number of different intial points for avoiding local optimization  
 Output : Yall = Matrix of corrected labels. 
          Yall(:,i)=  Corrected labels considering number of noisy data points in kList(i,:)
          spT = time spent for noise detection


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 function [Yall,spT]=LND_SLP1(K,kList,id_expert,Y)
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 Label Noise Detection method with Linear Sequentialization Algorithm with initial point for iterative algorithm is taken from output of LDN_spanno with level of approximation =1.


 Input :  K = Kernel matrix 
          KList = List of numbers of noisy points 
          kList(i,:) = Denote i^th try of various noisy ratio 
          kList(i,1) = Number of positively annotated datapoint is noisy.
          kList(i,2) = Number of negatively annotated datapoint is noisy.
          id_expert = List of index corresponding to expert annotation.
          Y = Annotation
          Num= Number of different intial points for avoiding local optimization  
 Output : Yall = Matrix of corrected labels. 
          Yall(:,i)=  Corrected labels considering number of noisy data points in kList(i,:)
          spT = time spent for noise detection



