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Created date
Jun 16, 2022 01:21 PM
category
Data Science
tags
Machine Learning
Machine Learning
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Manhattan Distance
- The sum of absolute differences
Application
- Used in the map to calculate the distance between two data points in a grid-like path
Mechanism
Minkowski distance
- A generalization of the Euclidean distance and the Manhattan distance.
- Is applied in machine learning to find out distance similarity.
Application
- To find the distance measured between 2 points in N-dimensional space
- See it as the correlation between 2 points
Mechanism
If C = 1 it is Manhattan Distance.
If C = 2 It is Euclidean distance.
Mahalanobis distance
Mahalanobis distance between two vectors, x and y, where S is the co-variance matrix.
- Co-variance of two feature indicated how values of two features are varying together.
- Measures how values of one feature are varying according to values of another feature.
- Uses inverse of co-variance matrix, that is why we have a T
Application
- Multivariate distance metric that measures the distance between a point (vector) and a distribution.
- Has excellent applications in multivariate anomaly detection, classification on highly imbalanced datasets and one-class classification and more untapped use cases
Mechanism
Further visit Distance - Understanding the math with examples (python)
Reference
- Author:Jason Siu
- URL:https://jason-siu.com/article%2Fb9b28b73-aa22-4d15-8141-5e4a844e8cb3
- Copyright:All articles in this blog, except for special statements, adopt BY-NC-SA agreement. Please indicate the source!
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