Non-Linear Transformation Operation

A Non-Linear Transformation Operation is a transformation operation that uses a non-linear function.

• AKA: Non-Linear Mapping.
• Example(s):
  • a Laplacian Eigenmap.
  • a Non-Linear Hidden Neural Network Layer.
  • …
• Counter-Example(s):
  • a Linear Mapping.
• See: Non-Linear Dimensionality Reduction.

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References

2010

• (Huh et al., 2010) ⇒ Seungil Huh, and Stephen E. Fienberg. (2010). “Discriminative Topic Modeling based on Manifold Learning.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835888
  • QUOTE: Traditional manifold learning algorithms [17, 14, 2] have given way to recent graph-based semi-supervised learning algorithms [19, 18, 3]. The goal of manifold learning is to recover the structure of a given dataset by non-linear mapping into a low-dimensional space. As a manifold learning algorithm, Laplacian Eigenmaps [2] was developed based on spectral graph theory [8].

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