Supervised Dimensionality Reduction Algorithm

An Supervised Dimensionality Reduction Algorithm is a dimensionality reduction algorithm that is a supervised learning algorithm and can solve a supervised dimensionality reduction task.

• Context:
  • It can be implemented into an Supervised Dimensionality Reduction System.
• Example(s):
  • (Melli et al., 2003).
  • (Baker & McCallum, 1998).
  • …
• Counter-Example(s):
  • an Unsupervised Dimensionality Reduction Algorithm.
• See: Compression.

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Context

1998

• (Baker & McCallum, 1998) ⇒ L. Douglas Baker, and Andrew McCallum. (1998). “Distributional Clustering of Words for Text Classification.” In: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. ISBN:1-58113-015-5 doi:10.1145/290941.290970
  • QUOTE: ... unlike some other unsupervised dimensionality-reduction techniques, such as Latent Semantic Indexing, we are able to compress the feature space much more aggressively, while still maintaining high document classification accuracy. ... significantly better than Latent Semantic Indexing [6], class-based clustering [1], feature selection by mutual information [23] or Markov-blanket-based feature selection [13].

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