 Similarity invariance

Similarity invariance, in a general sense, refers to the property of certain mathematical objects, functions, or systems that remain unchanged under specific transformations. The term can be applied in various fields, including geometry, statistics, and machine learning, among others. Here are a few contexts where similarity invariance is relevant: 1. **Geometry**: In geometry, similarity invariance often pertains to the properties of shapes that remain unchanged when objects are scaled, rotated, or translated.