Will larger inputs in one-hot encoding make more balance within weights?












1












$begingroup$


I was thinking if I have an input which has 36 possible values, and I make it as 36 inputs where exactly one of them is non 0, what is optimal value for each of the non 0 inputs?



It may be:



[1, 0, 0,....,0]

[0, 1, 0,....,0]

[0, 0, 1,....,0]


Or:



[36, 0, 0,....,0]

[0, 36, 0,....,0]

[0, 0, 36,....,0]


Or even:



[6, 0, 0,....,0]

[0, 6, 0,....,0]

[0, 0, 6,....,0]


In order this feature to have same impact on the network as any other feature with N(0,1) distribution by keeping in mind I will use L1 or L2 regularization



So for each weight on a normal input I will have 36 weights on those one-hot inputs, so regarding L1 those weights should be 36 times smaller in order to have same impact? Don't they?



But then their total impact in affecting result is small since only one of them is being multiplied by 1 and included in calculation...



So if you would be kind to explain this and convince me to use 1s instead 6s or 36s, please






neural-network feature-scaling







share|improve this question









$endgroup$

















    1












    $begingroup$


    I was thinking if I have an input which has 36 possible values, and I make it as 36 inputs where exactly one of them is non 0, what is optimal value for each of the non 0 inputs?



    It may be:



    [1, 0, 0,....,0]
    
[0, 1, 0,....,0]
    
[0, 0, 1,....,0]


    Or:



    [36, 0, 0,....,0]
    
[0, 36, 0,....,0]
    
[0, 0, 36,....,0]


    Or even:



    [6, 0, 0,....,0]
    
[0, 6, 0,....,0]
    
[0, 0, 6,....,0]


    In order this feature to have same impact on the network as any other feature with N(0,1) distribution by keeping in mind I will use L1 or L2 regularization



    So for each weight on a normal input I will have 36 weights on those one-hot inputs, so regarding L1 those weights should be 36 times smaller in order to have same impact? Don't they?



    But then their total impact in affecting result is small since only one of them is being multiplied by 1 and included in calculation...



    So if you would be kind to explain this and convince me to use 1s instead 6s or 36s, please






    neural-network feature-scaling







    share|improve this question









    $endgroup$















      1












      1








      1


      2



      $begingroup$


      I was thinking if I have an input which has 36 possible values, and I make it as 36 inputs where exactly one of them is non 0, what is optimal value for each of the non 0 inputs?



      It may be:



      [1, 0, 0,....,0]
      
[0, 1, 0,....,0]
      
[0, 0, 1,....,0]


      Or:



      [36, 0, 0,....,0]
      
[0, 36, 0,....,0]
      
[0, 0, 36,....,0]


      Or even:



      [6, 0, 0,....,0]
      
[0, 6, 0,....,0]
      
[0, 0, 6,....,0]


      In order this feature to have same impact on the network as any other feature with N(0,1) distribution by keeping in mind I will use L1 or L2 regularization



      So for each weight on a normal input I will have 36 weights on those one-hot inputs, so regarding L1 those weights should be 36 times smaller in order to have same impact? Don't they?



      But then their total impact in affecting result is small since only one of them is being multiplied by 1 and included in calculation...



      So if you would be kind to explain this and convince me to use 1s instead 6s or 36s, please






      neural-network feature-scaling







      share|improve this question









      $endgroup$




      I was thinking if I have an input which has 36 possible values, and I make it as 36 inputs where exactly one of them is non 0, what is optimal value for each of the non 0 inputs?



      It may be:



      [1, 0, 0,....,0]
      
[0, 1, 0,....,0]
      
[0, 0, 1,....,0]


      Or:



      [36, 0, 0,....,0]
      
[0, 36, 0,....,0]
      
[0, 0, 36,....,0]


      Or even:



      [6, 0, 0,....,0]
      
[0, 6, 0,....,0]
      
[0, 0, 6,....,0]


      In order this feature to have same impact on the network as any other feature with N(0,1) distribution by keeping in mind I will use L1 or L2 regularization



      So for each weight on a normal input I will have 36 weights on those one-hot inputs, so regarding L1 those weights should be 36 times smaller in order to have same impact? Don't they?



      But then their total impact in affecting result is small since only one of them is being multiplied by 1 and included in calculation...



      So if you would be kind to explain this and convince me to use 1s instead 6s or 36s, please






      neural-network feature-scaling




      neural-network feature-scaling






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










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      Djura MarinkovDjura Marinkov

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