Find the central feature in a list of vectors:

Find the central feature in a list of vectors with given weights:

Same inputs with different output formats:

Central feature works with WeightedData:

Central feature of a large array:

Weighted central feature:

Find the central feature of data involving quantities:

Find the central feature of a list of images:

List of pictures:

List of 3D images:

Compute the central feature of strings:

Compute the central feature of Boolean vectors:

Compute the central feature of a list of date objects:

Compute the central feature of geodetic positions:

Obtain a robust estimate of multivariate location when outliers are present:

Extreme values have a large influence on the Mean:

Sample points from a convex polygon:

Estimate the center of the polygon by computing the central feature of random points:

Find the central feature of California, based on the location of cities:

Find the central feature of California, based on the location of cities weighted by population:

Draw the cities' locations (gray), unweighted central feature (red) and weighted central feature (black):

The top eight largest cities in Ohio:

The central feature of the eight cities based on TravelDistance:

The sum of distances from the central feature to the other cities, based on TravelDistance:

Draw the cities' locations (gray) and the central feature (red):

CentralFeature is a multivariate location measure:

Mean is also a location measure:

Visualize the data points with central feature and mean:

CentralFeature finds a point belonging to the data that minimizes the sum of distances:

Compute the central feature directly from the definition:

Visualize the sum of distances function together with the data points:

CentralFeature is the same as Median with univariate data when the data length is odd:

CentralFeature finds an element in the data that minimizes the sum of distances to other data points:

SpatialMedian finds a point in the domain that minimizes the sum of distances:

The sum of distances with respect to CentralFeature is greater than or equal to the one with respect to SpatialMedian:

Create a random graph with edge weights sampled uniformly between 0 and 1:

Locate the GraphCenter:

Specify the distance between each pair of vertices using GraphDistance:

Locate the center using CentralFeature:

CentralFeature of a non-weighted, two-element list returns the first element:

For weighted two-element lists, it chooses the element with the highest weight, which trivially minimizes :