Matlab implements data normalization
Data normalization is an important method to transform different data into the same standard. When processing data, in many cases it is necessary to normalize the data for further analysis and comparison. Matlab provides many practical functions to normalize data.
(1) Min-Max Normalization
Min-max normalization is also called dispersion normalization, which is a linear function that maps the original data to between [0,1].
Formula: X_norm = (X - X_min)/(X_max - X_min)
% Use Matlab function to implement Min-Max Normalization
X = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
X_norm = (X - min(X))/(max(X) - min(X))
(2) Z-score Normalization
Z-score standardization, also known as standard deviation standardization, is a linear function that transforms raw data into a distribution with a mean of 0 and a standard deviation of 1.
Formula: X_norm = (X - mu)/sigma
% Use Matlab function to implement Z-score Normalization
X = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
X_norm = zscore(X)
(3) Decimal Scaling Normalization
Decimal scaling normalization is a nonlinear function that uses a proportional scale to reduce or expand the data so that all data falls between [-1,1] or [0,1].
Formula: X_norm = X/10^k, where k is a variable parameter, and the value is an integer such that the number with the largest absolute value is less than 1
% Use Matlab function to implement Decimal Scaling Normalization
X = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]; k
= ceil(log10(max(abs(X))))
X_norm = X /(10^k)
Summarize:
The above introduces several common data normalization methods and corresponding Matlab functions. Choosing an appropriate normalization method during data processing can improve the efficiency and accuracy of subsequent data analysis, and can also avoid some potential problems.