4 ms·
Maybe I misunderstood what you meant, but I would say that the L_2 norm amounts to MAD. L2 means sqrt(x²+y²+...) and in 1D, that becomes sqrt(x²) = |x|
by ced 13y ago
Maybe I misunderstood what you meant, but I would say that the L_2 norm amounts to MAD. L2 means
sqrt(x²+y²+...)
and in 1D, that becomes
sqrt(x²) = |x|
- klodolph 13y agoIn 1D, L_2 = L_1. But if you are talking about 100 sample points, the data has 100 dimensions.
- ced 13y agoAhhh, I see what you mean, I think. Or maybe not. If I have 3 sample points, [1,1,1], then the standard deviation is 0, but where you take your Euclidean distance? If I'm not mistaken, with 3 sample points a, b and c that have an average mu, then sigma = |(a, b, c) - (mu, mu, mu)| (L2 norm in R3) Is that the geometric interpretation you're referring to? It's neat, but the mu vector feels a bit artificial.