Signal-to-noise statistic
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In mathematics the signal-to-noise statistic distance between two vectors a and b with mean values and and standard deviation and respectively is:
In the case of Gaussian-distributed data and unbiased class distributions, this statistic can be related to classification accuracy given an ideal linear discrimination, and a decision boundary can be derived.[1]
This distance is frequently used to identify vectors that have significant difference. One usage is in bioinformatics to locate genes that are differential expressed on microarray experiments.[2][3][4]
See also[]
- Distance
- Uniform norm
- Manhattan distance
- Signal-to-noise ratio
- Signal to noise ratio (imaging)
Notes[]
- ^ Auffarth, B., Lopez, M., Cerquides, J. (2010). Comparison of redundancy and relevance measures for feature selection in tissue classification of CT images. Advances in Data Mining. Applications and Theoretical Aspects. p. 248--262. Springer.
- ^ Golub, T.R. et al. (1999) Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286, 531-537,
- ^ Slonim D.K. et al. (2000) Class Prediction and Discovery Using Gene Expression Data. Procs. of the Fourth Annual International Conference on Computational Molecular Biology Tokyo, Japan April 8 - 11, p263-272
- ^ Pomeroy, S.L. et al. (2002) Gene Expression-Based Classification and Outcome Prediction of Central Nervous System Embryonal Tumors. Nature 415, 436–442.
Categories:
- Statistical distance
- Statistical ratios
- Statistics stubs