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10 papers analyzed
These studies suggest that distance measures comparable to the Mahalanobis distance include the generalized Mahalanobis distance, Choquet-Mahalanobis integral, and Dynamic Time Warping.
20 papers analyzed
The Mahalanobis distance is a widely used measure in statistics and machine learning for determining the distance between a point and a distribution. It is particularly useful in scenarios where the data points are not independent, as it takes into account the correlations between variables. This synthesis explores various distance measures that can be compared to the Mahalanobis distance, drawing insights from multiple research papers.
Euclidean Distance:
Choquet Integral:
Dynamic Time Warping (DTW):
Large Margin Nearest Neighbor (LMNN):
Minimum Covariance Determinant and Minimum Volume Ellipsoid:
The Mahalanobis distance is a versatile measure that is often compared to other distance metrics like Euclidean distance, Choquet integral, Dynamic Time Warping (DTW), and Large Margin Nearest Neighbor (LMNN). Each of these measures has its own strengths and weaknesses, making them suitable for different types of data and applications. The Mahalanobis distance stands out for its ability to account for correlations between variables, making it particularly useful in multivariate analysis and scenarios involving dependent variables.
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