Frepresent
Frepresent is a framework used in data representation and machine learning to express complex objects as structured representations in a function space. It emphasizes representing features not just as isolated values but as components of a basis-driven, often linear, expansion. The concept is applicable across domains such as pattern recognition, time series, graphs, and multimedia, where efficient comparison and processing of objects depend on their feature representations.
Formally, Frepresent involves a feature map that projects an input x from a domain X into a
Variants of Frepresent include linear representations, where the basis functions are fixed and the coefficients directly
Applications of Frepresent span similarity search, classification, clustering, and data compression. Strengths include modularity, interpretability of
See also: feature representation, basis functions, kernel methods, Fourier features, wavelets.