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The Ethereal
A Generalization of Principal Component Analysis
October 29, 2019 ยท Declared Dead ยท ๐ IEEE International Conference on Acoustics, Speech, and Signal Processing
"No code URL or promise found in abstract"
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Authors
Samuele Battaglino, Erdem Koyuncu
arXiv ID
1910.13511
Category
cs.LG: Machine Learning
Cross-listed
eess.SP,
stat.ML
Citations
19
Venue
IEEE International Conference on Acoustics, Speech, and Signal Processing
Last Checked
4 months ago
Abstract
Conventional principal component analysis (PCA) finds a principal vector that maximizes the sum of second powers of principal components. We consider a generalized PCA that aims at maximizing the sum of an arbitrary convex function of principal components. We present a gradient ascent algorithm to solve the problem. For the kernel version of generalized PCA, we show that the solutions can be obtained as fixed points of a simple single-layer recurrent neural network. We also evaluate our algorithms on different datasets.
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