Open AccessProceedings Article
Generative Kernel PCA.
Joachim Schreurs,Johan A. K. Suykens +1 more
- 01 Jan 2018
11
TL;DR: A generative kernel PCA which can be used to generate new data, as well as denoise a given training dataset, in a non-probabilistic setting is introduced.
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Abstract: Kernel PCA has shown to be a powerful feature extractor within many applications. Using the Restricted Kernel Machine formulation, a representation using visible and hidden units is obtained. This enables the exploration of new insights and connections between Restricted Boltzmann machines and kernel methods. This paper explores these connections, introducing a generative kernel PCA which can be used to generate new data, as well as denoise a given training dataset. This in a non-probabilistic setting. Moreover, relations with linear PCA and a preimage reconstruction method are introduced in this paper.
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Citations
•Posted Content
Generative Restricted Kernel Machines: A Framework for Multi-view Generation and Disentangled Feature Learning
TL;DR: A novel framework for generative models based on Restricted Kernel Machines with joint multi-view generation and uncorrelated feature learning, called Gen-RKM, which has a primal and dual formulation to incorporate both kernel-based and (deep convolutional) neural network based models within the same setting.
Latent Space Exploration Using Generative Kernel PCA
David Winant,Joachim Schreurs,Johan A. K. Suykens +2 more
- 06 Nov 2019
TL;DR: The use of generative kernel PCA for exploring latent spaces of datasets is investigated and the use of the tool in combination with novelty detection is shown, where the latent space around novel patterns in the data is explored.
•Posted Content
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality
TL;DR: Experiments show that the weighted RKM is capable of generating clean images when contamination is present in the training data, and that the robust method also preserves uncorrelated feature learning through qualitative and quantitative experiments on standard datasets.
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality
Arun Pandey,Joachim Schreurs,Johan A. K. Suykens +2 more
- 19 Jul 2020
TL;DR: In this article, the authors introduce weighted conjugate feature duality in the framework of Restricted Kernel Machines (RKMs) to fine-tune the latent space of generative RKMs using a weighting function based on the Minimum Covariance Determinant, which is a robust estimator of multivariate location and scatter.
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•Posted Content
Generative Restricted Kernel Machines.
TL;DR: A novel framework for generative models based on Restricted Kernel Machines with multi-view generation and uncorrelated feature learning capabilities, called Gen-RKM, which is flexible to incorporate both kernel-based, (deep) neural network and Convolutional based models within the same setting.
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TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
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Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
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TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
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Heng Tao Shen
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TL;DR: The paper focuses on the use of principal component analysis in typical chemometric areas but the results are generally applicable.
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TL;DR: Principal component analysis (PCA) as discussed by the authors replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables.
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