Data Normalization Task
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A Data Normalization Task is a data preprocessing task that produces a normalized dataset.
- Context:
- It can be solved by a Data Normalization System, such as
X /= np.std(X, axis = 0)(in Python). - …
- It can be solved by a Data Normalization System, such as
- Counter-Example(s):
- See: Data Detrending, Data De-Meaning, Z-Space.
References
2016
- http://lamda.nju.edu.cn/weixs/project/CNNTricks/CNNTricks.html
- QUOTE: The first and simple pre-processing approach is zero-center the data, and then normalize them, which is presented as two lines Python codes as follows:
X -= np.mean(X, axis = 0) # zero-centerX /= np.std(X, axis = 0) # normalize
- where, X is the input data (NumIns×NumDim). Another form of this pre-processing normalizes each dimension so that the min and max along the dimension is -1 and 1 respectively. It only makes sense to apply this pre-processing if you have a reason to believe that different input features have different scales (or units), but they should be of approximately equal importance to the learning algorithm. In case of images, the relative scales of pixe
- QUOTE: The first and simple pre-processing approach is zero-center the data, and then normalize them, which is presented as two lines Python codes as follows: