Large scale inference in the Infinite Relational Model: Gibbs sampling is not enough

Albers, Kristoffer Jon; Moth, Andreas Leon Aagard; Mørup, Morten; Schmidt, Mikkel N.

doi:10.1109/mlsp.2013.6661904

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Due to the size of the networks, it is not computationally feasible to reach convergence. However, the Gibbs sampler quickly reach a stable cluster assignment with high posterior likelihood which is treated as a point estimate of the parameters (Albers et al, 2013 ). We hence treat the last sampled state as the inferred parameters.…”

Section: Methodsmentioning

confidence: 99%

Uncovering Cortical Units of Processing From Multi-Layered Connectomes

et al. 2022

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Modern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity can be derived. Unfortunately, the lack of observed correspondence between the connectivity profiles of the two modalities challenges the understanding of the relationship between the functional and structural connectome. Rather than focusing on correspondence at the level of connections we presently investigate correspondence in terms of modular organization according to shared canonical processing units. We use a stochastic block-model (SBM) as a data-driven approach for clustering high-resolution multi-layer whole-brain connectivity networks and use prediction to quantify the extent to which a given clustering accounts for the connectome within a modality. The employed SBM assumes a single underlying parcellation exists across modalities whilst permitting each modality to possess an independent connectivity structure between parcels thereby imposing concurrent functional and structural units but different structural and functional connectivity profiles. We contrast the joint processing units to their modality specific counterparts and find that even though data-driven structural and functional parcellations exhibit substantial differences, attributed to modality specific biases, the joint model is able to achieve a consensus representation that well accounts for both the functional and structural connectome providing improved representations of functional connectivity compared to using functional data alone. This implies that a representation persists in the consensus model that is shared by the individual modalities. We find additional support for this viewpoint when the anatomical correspondence between modalities is removed from the joint modeling. The resultant drop in predictive performance is in general substantial, confirming that the anatomical correspondence of processing units is indeed present between the two modalities. Our findings illustrate how multi-modal integration admits consensus representations well-characterizing each individual modality despite their biases and points to the importance of multi-layered connectomes as providing supplementary information regarding the brain's canonical processing units.

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Section: Methodsmentioning

confidence: 99%

Uncovering Cortical Units of Processing From Multi-Layered Connectomes

et al. 2022

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“…We also observe that Bayesian Plaid model does not necessarily obtain the perfect recovery even if K = K true . This may be explained by the fact that the MCMC inferences on BNP exhaustive bi-clustering models are easily trapped at local optimum in practice (Albers et al 2013). We expect the performance will be improved with more MCMC iterations.…”

Section: Synthetic Data Experimentsmentioning

confidence: 99%

Infinite Plaid Models for Infinite Bi-Clustering

Ishiguro

Sato

Nakano

et al. 2016

AAAI

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We propose a probabilistic model for non-exhaustive and overlapping (NEO) bi-clustering. Our goal is to extract a few sub-matrices from the given data matrix, where entries of a sub-matrix are characterized by a specific distribution or parameters. Existing NEO biclustering methods typically require the number of sub-matrices to be extracted, which is essentially difficult to fix a priori. In this paper, we extend the plaid model, known as one of the best NEO bi-clustering algorithms, to allow infinite bi-clustering; NEO bi-clustering without specifying the number of sub-matrices. Our model can represent infinite sub-matrices formally. We develop a MCMC inference without the finite truncation, which potentially addresses all possible numbers of sub-matrices. Experiments quantitatively and qualitatively verify the usefulness of the proposed model. The results reveal that our model can offer more precise and in-depth analysis of sub-matrices.

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Stochastic Blockmodeling

Doran

2017

SpringerBriefs in Complexity

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Large scale inference in the Infinite Relational Model: Gibbs sampling is not enough

Cited by 5 publications

References 8 publications

Uncovering Cortical Units of Processing From Multi-Layered Connectomes

Uncovering Cortical Units of Processing From Multi-Layered Connectomes

Infinite Plaid Models for Infinite Bi-Clustering

Stochastic Blockmodeling

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