비선형 차원축소

수학노트
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노트

  • It should be apparent, therefore, that NLDR has several applications in the field of computer-vision.[1]
  • Manifold Learning can be thought of as an attempt to generalize linear frameworks like PCA to be sensitive to non-linear structure in data.[2]
  • Tools for NLDR can help researchers across all areas of science and engineering to better understand and visualize their data.[3]
  • (2020) Predict high-frequency trading marker via manifold learning.[4]
  • Framework of Multiple-point Statistical Simulation Using Manifold Learning for the Dimensionality Reduction of Patterns.[4]
  • Joint Sparsity Aided Joint Manifold Learning for Sensor Fusion.[4]
  • Local distances preserving based manifold learning.[4]

소스

  1. ↑ Nonlinear dimensionality reduction
  2. ↑ 2.2. Manifold learning — scikit-learn 0.23.2 documentation
  3. ↑ A tractable latent variable model for nonlinear dimensionality reduction
  4. ↑ 4.0 4.1 4.2 4.3 Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

메타데이터

위키데이터

Spacy 패턴 목록

  • [{'LOWER': 'nonlinear'}, {'LOWER': 'dimensionality'}, {'LEMMA': 'reduction'}]
  • [{'LEMMA': 'NLDR'}]
  • [{'LOWER': 'manifold'}, {'LEMMA': 'learning'}]
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