Invertible Kernel PCA with Random Fourier Features
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916452287643648 |
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| author | Gedon, Daniel Ribeiro, Antôni H. Wahlström, Niklas Schön, Thomas B. |
| author_facet | Gedon, Daniel Ribeiro, Antôni H. Wahlström, Niklas Schön, Thomas B. |
| contents | Kernel principal component analysis (kPCA) is a widely studied method to construct a low-dimensional data representation after a nonlinear transformation. The prevailing method to reconstruct the original input signal from kPCA -- an important task for denoising -- requires us to solve a supervised learning problem. In this paper, we present an alternative method where the reconstruction follows naturally from the compression step. We first approximate the kernel with random Fourier features. Then, we exploit the fact that the nonlinear transformation is invertible in a certain subdomain. Hence, the name \emph{invertible kernel PCA (ikPCA)}. We experiment with different data modalities and show that ikPCA performs similarly to kPCA with supervised reconstruction on denoising tasks, making it a strong alternative. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_05043 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Invertible Kernel PCA with Random Fourier Features Gedon, Daniel Ribeiro, Antôni H. Wahlström, Niklas Schön, Thomas B. Machine Learning Signal Processing Kernel principal component analysis (kPCA) is a widely studied method to construct a low-dimensional data representation after a nonlinear transformation. The prevailing method to reconstruct the original input signal from kPCA -- an important task for denoising -- requires us to solve a supervised learning problem. In this paper, we present an alternative method where the reconstruction follows naturally from the compression step. We first approximate the kernel with random Fourier features. Then, we exploit the fact that the nonlinear transformation is invertible in a certain subdomain. Hence, the name \emph{invertible kernel PCA (ikPCA)}. We experiment with different data modalities and show that ikPCA performs similarly to kPCA with supervised reconstruction on denoising tasks, making it a strong alternative. |
| title | Invertible Kernel PCA with Random Fourier Features |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2303.05043 |