I am not sure if this question makes sense or is even ideal, but here is my problem: Many proposed distance formulas only give a distance in terms of 1-dimensions. A common distance formula is the Euclidean distance (the most obvious one). Euclidean distance $d_{ecdn}$ of two points located at $(x_1, x_2, x_3, … , x_n)$ and $(y_1, y_2, y_3, … , y_n)$, respectively, is given by

$d_{ecdn} = sqrt{displaystylesum_{i=1}^{n} (x_i-y_i)^2}$.

Geometrically, in any number of dimensions (but imagine 3 dimensions as an example), if you were to draw a line between the two points, whose length expresses the Euclidean distance, the line itself exists in 1 dimension. Why is this a problem? I am doing a project involve data mining and information retrieval. Basically, each document in a corpus is mapped to a location in an $n$-dimensional space where $n$ is the total number of terms within the whole set of documents. They say that if document A is geometrically closer to document B than document C, then document A is more similar to B than C. Euclidean distance gives the wrong impression of similarity since each dimension matters.

I am wondering if anyone has any good algorithms that will calculate the "distance" between two points, but with a weight on each dimensions. An example would be the Pearson Product-Moment Correlation Coefficient. This formula is given by

**Contents**hide

#### Best Answer

Just to create an official answer for this topic (based on @TenaliRaman's comment), you want the Mahalanobis distance.

This is computed similarly to the Euclidian distance, (in fact, the Euclidian distance formula is a special case of the Mahalanobis distance formula) except each squared difference term is 'corrected' based on an inverted covariance matrix.

From the Wikipedia page:

` Mahalanobis distance is thus unitless and scale-invariant, and takes into account the correlations of the data set. `

### Similar Posts:

- Solved – Difference between Euclidean, Pearson, Geodesic and Mahalanobis distance metrics
- Solved – Mahalanobis distance via PCA when $n<p$
- Solved – Mahalanobis distance via PCA when $n<p$
- Solved – Similarity scoring to compare multi-dimensional datasets
- Solved – Is Mahalanobis distance dependent from the vector dimensions