arXiv:2607. 14081v1 Announce Type: new Abstract: Linear Independent Component Analysis (ICA) recovers jointly independent source signals from their linear mixtures.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
Linear Independent Component Analysis (ICA) recovers jointly independent source signals from their linear mixtures. To achieve this, classical ICA algorithms attempt to maximize non-Gaussianity, measured by negentropy, which is linked to independence by information theory.
arXiv:2608. 13229v1 Announce Type: cross Abstract: We present the mathematical foundations of linear independent component analysis (ICA) models based on standard literature in a self-contained note.
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arXiv:2606. 07914v1 Announce Type: cross Abstract: We study component recovery and mixing-matrix estimation from unlabeled finite mixtures whose observable distributions share the same latent components but have unknown mixing weights.
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