November 2023
Intermediate to advanced
576 pages
13h 30m
German
Content preview from Handbuch Data Science mit PythonBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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KAPITEL 46
Ausführlich: Manifold Learning
Im vorherigen Kapitel haben Sie gesehen, wie die Hauptkomponentenanalyse zur Dimensionsreduktion eingesetzt werden kann – sie reduziert die Anzahl der Features einer Datenmenge, bewahrt dabei jedoch die wesentlichen Beziehungen zwischen den Datenpunkten. Die PCA ist zwar flexibel, schnell und leicht interpretierbar, funktioniert aber nicht besonders gut, wenn in den Daten nichtlineare Beziehungen vorhanden sind. Im Folgenden werden wir einigen Beispielen hierfür begegnen.
Um diese Schwäche zu umgehen, können wir uns den Manifold-Learning-Algorithmen zuwenden – einer Klasse unüberwachter Estimators, die versuchen, Datenmengen als in hochdimensionale Räume eingebettete niedrigdimensionale Mannigfaltigkeiten ...
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