Hi everyone,
May I know which way correct to do normalization for a matrix by colums or rows?
Thanks.

2 个评论

The correct way depends on what you are trying to do, how are you normalizing etc. You need to specify what is your purpose for normalizing the matrix.
Thanks for your reply. I wanna do classification using SVM. The dimension of the matrix is 242* 256.

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Jan
Jan 2018-5-26
编辑:Jan 2018-5-26
x = rand(242, 256),
xNRow = x ./ sum(x, 2); % Auto-expanding, Matlab >= R2016b
xNCol = x ./ sum(x, 1);
For older Matlab versions:
xNRow = bsxfun(@rdivide, x, sum(x, 2));
xNCol = bsxfun(@rdivide, x, sum(x, 1));
Now the rows or columns are normalized, such the the sum is 1.0. But perhaps you want the norm to be 1.0?
xNRow = x ./ vecnorm(x, 2); % Auto-expanding, vecnorm needs >= R2017b
xNCol = x ./ vecnorm(x, 1);
Or with older Matlab versions:
xNRow = x ./ sqrt(sum(x .* x), 2)); % Auto-expanding, >= R2016b
xNCol = x ./ sqrt(sum(x .* x), 1));
or again with bsxfun.
There are more methods for a "normalization": Set the mean to zero, and/or the std to 1 or such that the maximum peak height is 1.0. So you have to find out, what you need mathematically. Then the implementation in Matlab is easy.

2 个评论

Thanks for kind information. It seems that the normaliztion is done for both colums or rows. Is it okey to do normaliztion for colums only.Because when i do it for rows only the performace is getting low but with colums it is very high. That's why am asking which one correct rwos or colums. Thanks again
I have used the following formula to do normalaztion
minData=min(min(Class1_feature))
maxData=max(max(Class1_feature));
Class1_feature=((Class1_feature-minData)./(maxData));

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