Multi-View Independent Component Analysis with Shared and Individual Sources

• URL: http://arxiv.org/abs/2210.02083v1
• Date: Wed, 5 Oct 2022 08:23:05 GMT
• Title: Multi-View Independent Component Analysis with Shared and Individual Sources
• Authors: Teodora Pandeva, Patrick Forr\'e
• Abstract summary: Independent component analysis (ICA) is a blind source separation method for linear disentanglement of independent latent sources from observed data. We prove that the corresponding linear structure is identifiable, and the shared sources can be recovered, provided that sufficiently many diverse views and data points are available. We show empirically that our objective recovers the sources in high dimensional settings, also in the case when the measurements are corrupted by noise.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Independent component analysis (ICA) is a blind source separation method for linear disentanglement of independent latent sources from observed data. We investigate the special setting of noisy linear ICA where the observations are split among different views, each receiving a mixture of shared and individual sources. We prove that the corresponding linear structure is identifiable, and the shared sources can be recovered, provided that sufficiently many diverse views and data points are available. To computationally estimate the sources, we optimize a constrained form of the joint log-likelihood of the observed data among all views. We show empirically that our objective recovers the sources in high dimensional settings, also in the case when the measurements are corrupted by noise. Finally, we apply the proposed model in a challenging real-life application, where the estimated shared sources from two large transcriptome datasets (observed data) provided by two different labs (two different views) lead to a more plausible representation of the underlying graph structure than existing baselines.

Related papers

• MultiView Independent Component Analysis with Delays [72.19163346293848]
  We present MultiView Independent Component Analysis with Delays (MVICAD) MVICAD builds on the MultiView ICA model by allowing sources to be delayed versions of some shared sources. As ICA is often used in neuroscience, we show that latencies are age-related when applied to Cam-CAN, a large-scale magnetoencephalography (MEG) dataset.
  arXiv  Detail & Related papers  (2023-12-01T10:33:16Z)
• Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources [64.96984404868411]
  We address the problem of integrating data from multiple, possibly biased, observational and interventional studies. We show that the likelihood of the available data has no local maxima. We then show how the same approach can address the general case of multiple datasets.
  arXiv  Detail & Related papers  (2023-07-31T11:28:24Z)
• sJIVE: Supervised Joint and Individual Variation Explained [0.0]
  Analyzing multi-source data, which are multiple views of data on the same subjects, has become increasingly common in biomedical research. We propose a method called supervised joint and individual variation explained (sJIVE) that can simultaneously identify shared (joint) and source-specific (individual) underlying structure.
  arXiv  Detail & Related papers  (2021-02-26T02:54:45Z)
• Rethinking Content and Style: Exploring Bias for Unsupervised Disentanglement [59.033559925639075]
  We propose a formulation for unsupervised C-S disentanglement based on our assumption that different factors are of different importance and popularity for image reconstruction. The corresponding model inductive bias is introduced by our proposed C-S disentanglement Module (C-S DisMo) Experiments on several popular datasets demonstrate that our method achieves the state-of-the-art unsupervised C-S disentanglement.
  arXiv  Detail & Related papers  (2021-02-21T08:04:33Z)
• InSRL: A Multi-view Learning Framework Fusing Multiple Information Sources for Distantly-supervised Relation Extraction [19.176183245280267]
  We introduce two widely-existing sources in knowledge bases, namely entity descriptions and multi-grained entity types. An end-to-end multi-view learning framework is proposed for relation extraction via Intact Space Representation Learning (InSRL)
  arXiv  Detail & Related papers  (2020-12-17T02:49:46Z)
• Out-of-distribution Generalization via Partial Feature Decorrelation [72.96261704851683]
  We present a novel Partial Feature Decorrelation Learning (PFDL) algorithm, which jointly optimize a feature decomposition network and the target image classification model. The experiments on real-world datasets demonstrate that our method can improve the backbone model's accuracy on OOD image classification datasets.
  arXiv  Detail & Related papers  (2020-07-30T05:48:48Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)
• Bayesian Sparse Factor Analysis with Kernelized Observations [67.60224656603823]
  Multi-view problems can be faced with latent variable models. High-dimensionality and non-linear issues are traditionally handled by kernel methods. We propose merging both approaches into single model.
  arXiv  Detail & Related papers  (2020-06-01T14:25:38Z)
• Source Separation with Deep Generative Priors [17.665938343060112]
  We use generative models as priors over the components of a mixture of sources, and noise-annealed Langevin dynamics to sample from the posterior distribution of sources given a mixture. This decouples the source separation problem from generative modeling, enabling us to directly use cutting-edge generative models as priors. The method achieves state-of-the-art performance for MNIST digit separation.
  arXiv  Detail & Related papers  (2020-02-19T00:48:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.