Independent Component Analysis¶
Hyvärinen, A., & Oja, E. (2000). Independent Component Analysis: Algorithms and Applications. Neural Networks, 13(4), 4-5.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Signal Extraction
- In statistics and machine learning, regression decomposes outcomes into a fitted signal and a residual, PCA and ICA decompose a high-dimensional observation into target components and discarded variance, and denoising autoencoders learn the signal model implicitly.
This sourceCanonical reference for ICA and blind source separation (the cocktail-party problem) decomposing observations into target components and discarded variance.
Supported in partVerified against the work's full text
Covers the ICA/PCA half: ICA recovers latent components from mixed observations, and PCA-style preprocessing discards small-variance directions. Nothing on regression or autoencoders.
“eigenvalues dj of E{xxT} and discard those that are too small, as is often done in the statistical technique of princi- pal component analysis.”
- In statistics and machine learning, regression decomposes outcomes into a fitted signal and a residual, PCA and ICA decompose a high-dimensional observation into target components and discarded variance, and denoising autoencoders learn the signal model implicitly.
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Registry ID ref:af29037c9ede · see in the full table