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Home > Machine Learning Guide > 032 Cartesian Similarity Metrics
Podcast: Machine Learning Guide
Episode:

032 Cartesian Similarity Metrics
Category: Technology
Duration: 00:42:28
Publish Date: 2020-11-08 05:10:52
Description:

Social media Gnothi and email me a screenshot/link for 3-month access to Machine Learning Applied; commit code to the Github repository for life-access.

Normed distances link

  • A norm is a function that assigns a strictly positive length to each vector in a vector space. link
  • Minkowski is generalized. p_root(sum(xi-yi)^p). "p" = ? (1, 2, ..) for below.
  • L1: Manhattan/city-block/taxicab. abs(x2-x1)+abs(y2-y1). Grid-like distance (triangle legs). Preferred for high-dim space.
  • L2: Euclidean. sqrt((x2-x1)^2+(y2-y1)^2. sqrt(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 link. Dot product: inner product on a finite dimensional Euclidean space link

Cosine (normalized dot)
Total Play: 2

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