1. Curse of Dimensionality: different narrations
Similarity function: Almost all pairs of points are equally far away from one another, and almost any two vectors are almost orthogonal.
Statistical Inference: This sparsity is problematic for any method that requires statistical significance. In order to obtain a statistically sound and reliable result, the amount of data needed to support the result often grows exponentially with the dimensionality. Think about the features are supposed to be IDPT in a simple regression problem.
General ML: Given finite number of data samples in a high-dimensional feature space with each feature having a number of possible values, an enormous amount of training data is required to ensure that there are several samples with each combination of values.
