The Emergence Machine

Feature Extraction

process · Computing · Level 7 · E10

E10Institutions

Each concept here is mapped to its prerequisites — the ideas you'd need first to understand it — all the way down to four foundations: Space, Time, Energy, Pattern. Click any prerequisite to drill down, or scroll for the chain graph.

Trace. Question. Emerge.

Emergence definition

Feature extraction is the process of selecting or deriving relevant features from raw data to improve machine learning model performance and efficiency, which builds upon the understanding of spatial relationships and patterns of more/less/equal, and the expression of measurable magnitudes and amounts within the three-dimensional expanse.

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Wiktionary senses

External reference — all senses of the word “feature extraction” on Wiktionary. This atlas concept maps to only the slice of meaning relevant to the prerequisite graph.

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Source: Wiktionary — “feature extraction”. Content available under CC BY-SA 4.0.

Historical origin

Origin word
feature extraction
Origin language
English

Prerequisite chain

Possible path of this concept down to the fundamental substrate.

thisfoundationsL7L6L5L4L3L2L1L0Feature ExtractionComputerMachineDataToolMaterialSoftwareSystemFeatureForceFormInformationActionChangeMatterMotionEnergyPatternSpaceTimeE1 concrete → E14 abstract

Neighborhood

Direct prerequisites above, concepts that depend on this one below.

thisprerequisitesFeature ExtractionL7PatternL0FeatureL2DataL4ComputerL6E1 concrete → E14 abstract

In other languages

Prerequisites

What you need to understand first.

  • Pattern L0 (requires)
    Understanding pattern is essential for grasping the distinctive characteristics or attributes of a thing or person that emerge from the inherent nature of matter and radiation within the spatial context.
  • Feature L2 (requires)
    feature extraction builds on the concept of feature
  • Data L4 (requires)
    Feature extraction is the process of selecting or deriving relevant features from raw data to improve machine learning model performance and efficiency.
  • Computer L6 (requires)
    feature extraction builds on the concept of computer