Nonlinear Independent Component Analysis Using Ensemble Learning: Experiments and Discussion

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NICE: Non-linear Independent Components Estimation (2014, methodology): “of log-likelihood, the variational lower bound, is used more successfully in nonlinear independent components analysis (Hyv¨arinen and Pajunen, 1999) via ensemble learning (Roberts and Everson, 2001; Lappalainen et al., 2000) and in (Kingma and Welling, 2014; Rezende et al., 2014) using a type of He”
An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models (2002, methodology): “Our earlier experiments (Honkela & Karhunen, 2001; Hyv¨arinen et al., 2001; Lappalainen & Honkela, 2000; Valpola et al., 2000) with the static nonlinear factor analysis (NFA) and nonlinear independent factor analysis (NIFA) methods indicated that 7500 sweeps were sufﬁcient for learning the factors o”
Kernel-Based Nonlinear Blind Source Separation (2003, background): “, 1997), extensions of generative topographic mapping (GTM; Pajunen & Karhunen, 1997), neural networks (Burel, 1992; Marques & Almeida, 1999), or Bayesian ensemble learning (Valpola et al., 2000; Iline, Valpola, & Oja, 2001; Lappalainen & Honkela, 2000) to unfold the nonlinearity f.”
Advances in Nonlinear Blind Source Separation (2003, methodology): “Experimental results with artificially generated data, showing that the NFA method followed by linear ICA and the NIFA method are able to approximate pretty well the true sources, have been presented in [1], [62], [64].”