# Feature scaling

**Feature scaling** is a method used to standardize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step.

## Motivation

Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work properly without normalization {{ safesubst:#invoke:Unsubst||date=__DATE__ |$B=
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Another reason why feature scaling is applied is that gradient descent converges much faster with feature scaling than without it.

## Methods

### Rescaling

The simplest method is rescaling the range of features to make the features independent of each other and aims to scale the range in [0, 1] or [−1, 1]. Selecting the target range depends on the nature of the data. The general formula is given as:

where is an original value, is the normalized value. For example, suppose that we have the students' weight data, and the students' weights span [160 pounds, 200 pounds]. To rescale this data, we first subtract 160 from each student's weight and divide the result by 40 (the difference between the maximum and minimum weights).

### Standardization

In machine learning, we can handle various types of data, e.g., audio signals, pixel values for image data, and etc., and this data can include multiple dimensions. Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the enumerator) and unit-variance. This method is widely used for normalization in many machine learning algorithms (e.g., support vector machines, logistic regression, and neural networks) {{ safesubst:#invoke:Unsubst||date=__DATE__ |$B=
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### Scaling to unit length

Another option that is widely used in machine-learning is to scale the components of a feature vector such that the complete vector has length one. This usually means dividing each component by the Euclidean length of the vector. In some applications (e.g. Histogram features) it can be more practical to use the L1 norm (i.e. Manhattan Distance, City-Block Length or Taxicab Geometry) of the feature vector:

This is especially important if in the following learning steps the Scalar Metric is used as a distance measure.

## Application

In stochastic gradient descent, feature scaling can sometimes improve the convergence speed of the algorithm {{ safesubst:#invoke:Unsubst||date=__DATE__ |$B=
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## References

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- S. Aksoy and R. Haralick, “Feature normalization and likelihood-based similarity measures for image retrieval,” Pattern Recognit. Lett., Special Issue on Image and Video Retrieval, 2000 http://www.cs.bilkent.edu.tr/~saksoy/papers/prletters01_likelihood.pdf

- S. Tsakalidis, V. Doumpiotis & W. Byrne, “Discriminative Linear Transforms for Feature Normalization and Speaker Adaptation in HMM Estimation”, Proc. ICSLP'02, Denver. http://malach.umiacs.umd.edu/pubs/VD_05_Discrim_linear.pdf

- Liefeng Bo, Ling Wang, and Licheng Jiao, “Feature Scaling for Kernel Fisher Discriminant Analysis Using Leave-one-out Cross Validation”, Neural Computation (NECO), vol. 18(4), pp. 961–978, 2006 http://www.cs.washington.edu/homes/lfb/paper/nc06.pdf

- A. Stolcke, S. Kajarekar, and L. Ferrer, “Nonparametric feature normalization for SVM-based speaker verification,” in Proc. ICASSP, Las Vegas, Apr. 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4517925

- E. Youn, M. K. Jeong, “Class dependent feature scaling method using naive Bayes classifier for text datamining” Pattern Recognition Letters, 2009. http://www.sciencedirect.com/science/article/pii/S0167865508003553

- S. Theodoridis, K. Koutroumbas. (2008) “Pattern Recognition”, Academic Press, 4 edition, ISBN 978-1-59749-272-0