About use of Manhattan Distance when dimensionality increases:
The Manhattan distance calculation can become less effective and lose its discriminating power. This is because the Manhattan distance is calculated by summing the absolute differences between the coordinates of two points along each dimension. When the number of dimensions is large, the absolute differences along each dimension tend to become smaller and the contribution of each dimension to the overall distance decreases. This can make it harder to distinguish between different data points, as their Manhattan distances may be similar even if they are actually quite dissimilar.
How is the statement valid w.r.t the article which says to favor Manhattan distance over others when there is fixed or high value of dimensionality.