Calcul neuromimétique et traitement du signal : analyse en composantes indépendantes

By C. Jutten. 1987

Subfields: Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Representation & Self-Supervised Learning

1. Juha Karhunen
2. Christian Jutten
3. Lucas C. Parra
4. Clay D. Spence
5. Aapo Hyvärinen
6. Chengjun Liu
7. J. Urgen Schmidhuber
8. Patrik O. Hoyer

Citations in the corpus

• Beyond CCA: Moment Matching for Multi-View Models (2016, neutral): “A notable identifiable version of FA is independent component analysis (ICA; Jutten, 1987; Jutten & H´erault, 1991; Comon & Jutten, 2010).”
• Rethinking LDA: Moment Matching for Discrete ICA (2015, neutral): “The only difference between (4) and the standard ICA model [6, 7, 8] (without additive noise) is the presence of the Poisson noise which enforces discrete, instead of continuous, values of xm.”
• Advances in Nonlinear Blind Source Separation (2003, methodology): “Already in 1987, Jutten [76] used soft nonlinear mixtures”
• Convolutive blind separation of non-stationary sources (2000, background): “Recall now equation (1) of the convolutive case: x(t) = A s(t) + n(t) : (20) As suggested for other source separation algorithms, our approach to the convolutive case is to transform the problem into the frequency domain and to solve simultaneously a separation problem for every frequency [34], [35]”