Latent Space Exploration Using Generative Kernel PCA
Kernel PCA is a powerful feature extractor which recently has seen a reformulation in the context of Restricted Kernel Machines (RKMs). These RKMs allow for a representation of kernel PCA in terms of hidden and visible units similar to Restricted Boltzmann Machines. This connection has led to insights on how to use kernel PCA in a generative procedure, called generative kernel PCA. In this paper, the use of generative kernel PCA for exploring latent spaces of datasets is investigated. New points can be generated by gradually moving in the latent space, which allows for an interpretation of the components. Firstly, examples of this feature space exploration on three datasets are shown with one of them leading to an interpretable representation of ECG signals. Afterwards, the use of the tool in combination with novelty detection is shown, where the latent space around novel patterns in the data is explored. This helps in the interpretation of why certain points are considered as novel.
Code (0)
등록된 구현이 없습니다.
Tasks
Novelty DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks
Generative Adversarial Networks (GANs) are an emerging form of indirect encoding. The GAN is trained to induce a latent space on training data, and a real-valued evolutionary algorithm can search that latent space. Such …
Tensor-based Multi-view Spectral Clustering via Shared Latent Space
Multi-view Spectral Clustering (MvSC) attracts increasing attention due to diverse data sources. However, most existing works are prohibited in out-of-sample predictions and overlook model interpretability and exploratio…
ClusteringLatent Diffusion : Multi-Dimension Stable Diffusion Latent Space Explorer
Latent space is one of the key concepts in generative AI, offering powerful means for creative exploration through vector manipulation. However, diffusion models like Stable Diffusion lack the intuitive latent vector con…
Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model
We introduce a Deep Kernel Learning Variational Autoencoder (VAE-DKL) framework that integrates the generative power of a Variational Autoencoder (VAE) with the predictive nature of Deep Kernel Learning (DKL). The VAE le…
Property PredictionBlind Image Deconvolution by Generative-based Kernel Prior and Initializer via Latent Encoding
Blind image deconvolution (BID) is a classic yet challenging problem in the field of image processing. Recent advances in deep image prior (DIP) have motivated a series of DIP-based approaches, demonstrating remarkable s…
Generative Adversarial NetworkImage Deconvolution