Podcasts Technology Machine Learning Guide MLG 032 Cartesian Similarity Metrics

MLG 032 Cartesian Similarity Metrics

Published: Nov. 8, 2020, 5 p.m.

b'Show notes at ocdevel.com/mlg/32.

L1/L2 norm, Manhattan, Euclidean, cosine distances, dot product

Normed distances\\xa0link

  • A norm is a function that assigns a strictly positive length to each vector in a vector space.\\xa0link
  • Minkowski is generalized.\\xa0p_root(sum(xi-yi)^p). "p" = ? (1, 2, ..) for below.
  • L1: Manhattan/city-block/taxicab.\\xa0abs(x2-x1)+abs(y2-y1). Grid-like distance (triangle legs). Preferred for high-dim space.
  • L2: Euclidean.\\xa0sqrt((x2-x1)^2+(y2-y1)^2.\\xa0sqrt(dot-product). Straight-line distance; min distance (Pythagorean triangle edge)
  • Others: Mahalanobis, Chebyshev (p=inf), etc

Dot product

  • A type of inner product.
    Outer-product: lies outside the involved planes. Inner-product: dot product lies inside the planes/axes involved\\xa0link. Dot product: inner product on a finite dimensional Euclidean space\\xa0link

Cosine (normalized dot)

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