Advances in Nonlinear Blind Source Separation

By Christian Jutten, Juha Karhunen. 2003

165 citations recorded by the source corpus.

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A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation (2020, methodology): “In a similar spirit to disentanglement, (non-)linear independent component analysis (Comon, 1994; Bach and Jordan, 2002; Jutten and Karhunen, 2003; Hyvarinen and Morioka, 2016) studies the problem of recovering independent components of a signal.”
On the challenges of learning with inference networks on sparse, high-dimensional data (2017, background): “The assumption of linearity has been relaxed in nonlinear factor analysis (NFA) (Gibson, 1960) and extended across a variety of domains such as economics (Jones, 2006), signal processing (Jutten & Karhunen, 2003), and machine learning (Valpola & Karhunen, 2002; Lawrence, 2003).”
On the Transfer of Disentangled Representations in Realistic Settings (2020, background): “Ideas related to disentangling the factors of variation date back to the non-linear ICA literature (Comon, 1994; Hyv¨arinen and Pajunen, 1999; Bach and Jordan, 2002; Jutten and Karhunen, 2003; Hyvarinen and Morioka, 2016; Hyvarinen et al., 2019; Gresele et al., 2019).”
On the Fairness of Disentangled Representations (2019, background): “Ideas related to disentangling the factors of variations have a long tradition in machine learning, dating back to the non-linear ICA literature [17, 3, 46, 42, 43, 44, 32].”
Are Disentangled Representations Helpful for Abstract Visual Reasoning? (2019, background): “Other Related Works Learning disentangled representations is similar in spirit to non-linear ICA, although it relies primarily on (architectural) inductive biases and different degrees of supervision [13, 2, 39, 36, 37, 38, 25, 33, 32].”
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (2018, background): “4 Other related work In a similar spirit to disentanglement, (non-)linear independent component analysis [12, 2, 25, 22] studies the problem of recovering independent components of a signal.”
Isolating Sources of Disentanglement in VAEs (2018, background): “In a similar vein, non-linear independent component analysis [36, 37, 38] studies the problem of inverting a generative process assuming independent latent factors.”
Disentangling Factors of Variation Using Few Labels (2019, background): “[29] Christian Jutten and Juha Karhunen.”
Extensions of ICA for Causality Discovery in the Hong Kong Stock Market (2006, unknown): “It was ever believed that smooth mappings provided by MLP’s are sufficient to ensure that nonlinear ICA leads to nonlinear BSS [1], but a counterexample against this conjecture has been given in [ 7 ].”
Linear and nonlinear ICA based on mutual information (2007, background): “The PNL ICA In the post-nonlinear (PNL) model, the nonlinear observations have the following specific form[6].”
On the Analysis and Interpretation of Inhomogeneous Quadratic Forms as Receptive Fields (2006, background): “…receptive–ﬁeld estimates in physiological studies (e.g. Touryan et al., 2002; Rust et al., 2004) and information theoretical models like principal component analysis (PCA) (Sch¨olkopf et al., 1998) and independent component analysis (ICA) (see (Jutten and Karhunen, 2003) for a review).”
Nonparametric Identification and Semiparametric Estimation of Classical Measurement Error Models Without Side Information (2012, background): “Some nonlinear factor models have also been considered in Bauer (2005), Yalcin and Amemiya (2001), and Jutten and Karhunen (2003), however, this strand of the literature has largely bypassed the question of identification or has focused on specific cases (such as nonlinear models that can be…”
Isolating Sources of Disentanglement in Variational Autoencoders (2018, background): “In a similar vein, non-linear independent component analysis [36, 37, 38] studies the problem of inverting a generative process assuming independent latent factors.”