Unsupervised Ensemble Algorithm

An Unsupervised Ensemble Algorithm is an Unsupervised Learning Algorithm that is an Ensemble-based Algorithm.

• See: Ensemble Algorithm, Unsupervised Learning Algorithm, Supervised Ensemble Algorithm, Clustering Model.

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References

2002

• (Valpola & Karhunen, 2002) ⇒ Harri Valpola, and Juha Karhunen. (2002). “An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models.” In: Neural Comput., 14(11). doi:10.1162/089976602760408017.
  • QUOTE: Ensemble learning (Barber and Bishop, 1998; Lappalainen and Miskin, 2000), also called variational Bayes, is a method developed recently (Hinton and van Camp, 1993; MacKay, 1995) for approximating the posterior density (2). It can be used both for parametric and variationalapproximation. In the former, some parameters characterizing the posterior pdf are optimized while in the latter, a whole function is optimized. More specifically, ensemble learning employs the Kullback-Leibler (KL) divergence (also called KL distance or information) between two probability distributions q(v) and p(v).

1999

• (Lappalainen, 1999) ⇒ H. Lappalainen. (1999). “Ensemble Learning for Independent Component Analysis.” In: ProceedingsInternational Workshop on Independent Component Analysis and Signal Separation (ICA 1999).

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