Hierarchical sparse modeling using Spike and Slab priors

Suo, Yuanming; Dao, Minh N.; Tran, Trac D.; Srinivas, Umamahesh; Monga, Vishal

doi:10.1109/icassp.2013.6638229

Cited by 11 publications

(12 citation statements)

References 17 publications

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“…These methods operate on a single sample set where inducing sparsity at the group level on covariates is desired. In contrast, Suo et al [2013] andMousavi et al [2014] demonstrate the use of spike-and-slab priors for variable selection on a single covariate set shared by multiple sample sets for classification purposes. Hierarchical variable selection has also been considered in Bayesian survival models, as is done in Lee and Mallick [2004] and Lee et al [2014], which both present the use of spike-and-slab priors in proportional hazards models.…”

Section: Bayesian Hierarchical Spike-and-slab Survival Modelmentioning

confidence: 99%

A Hierarchical Spike-and-Slab Model for Pan-Cancer Survival Using Pan-Omic Data

Samorodnitsky¹,

Hoadley²,

Lock³

2021

Preprint

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Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer, expanding what was known from single-cancer or single-omics studies. However, pan-cancer, pan-omics analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict important clinical outcomes, like overall survival. We address the issue of prediction across multiple highdimensional sources of data and multiple sample sets by using exploratory results from BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a predictive model. We apply a Bayesian hierarchical model that performs variable selection using spike-andslab priors which are modified to allow for the borrowing of information across clustered data. This method is used to predict overall patient survival from the Cancer Genome Atlas (TCGA) using data from 29 cancer types and 4 omics sources. Our model selected patterns of variation identified by BIDIFAC+ that differentiate clinical tumor subtypes with markedly different survival outcomes. We also use simulations to evaluate the performance of the modified spike-andslab prior in terms of its variable selection accuracy and prediction accuracy under different underlying data-generating frameworks. Software and code used for our analysis can be found at https://github.com/sarahsamorodnitsky/HierarchicalSS PanCanPanOmics/.

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Section: Bayesian Hierarchical Spike-and-slab Survival Modelmentioning

confidence: 99%

A Hierarchical Spike-and-Slab Model for Pan-Cancer Survival Using Pan-Omic Data

Samorodnitsky¹,

Hoadley²,

Lock³

2021

Preprint

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“…With this assumption our framework can capture more general notions of structure and sparsity in the matrix X X X. This is one of our central analytical contributions in this paper, in contrast with methods with relaxed and simplified assumptions [19,28]. The benefit of using the Bayesian approach is that it can alleviate the burden on requirement of abundant training.…”

Section: Bayesian Frameworkmentioning

confidence: 99%

Multi-task image classification via collaborative, hierarchical spike-and-slab priors

Mousavi

Srinivas

Monga

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

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Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC). Recently, it has been shown that the use of class-specific spike-and-slab priors in conjunction with the class-specific dictionaries from SRC is particularly effective in low training scenarios. As a logical extension, we build on this framework for multitask scenarios, wherein multiple representations of the same physical phenomena are available. We experimentally demonstrate the benefits of mining joint information from different camera views for multi-view face recognition.

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“…Examples of such priors are Laplacian [30], generalized Pareto [31], Spike and Slab [32], etc. Amongst these priors, a well-suited sparsity promoting prior is spike and slab prior which is widely used in sparse recovery and Bayesian inference for variable selection and regression [17], [20], [33], [34]. In fact, it is acknowledged that spike and slab prior is indeed the gold standard for inducing sparsity in Bayesian inference [35].…”

Section: Introductionmentioning

confidence: 99%

Iterative Convex Refinement for Sparse Recovery

Mousavi

Monga

Tran

2015

IEEE Signal Process. Lett.

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Abstract-In this letter, we address sparse signal recovery using spike and slab priors. In particular, we focus on a Bayesian framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. The optimization resulting from spike and slab prior maximization is known to be a hard non-convex problem, and existing solutions involve simplifying assumptions and/or relaxations. We propose an approach called Iterative Convex Refinement (ICR) that aims to solve the aforementioned optimization problem directly allowing for greater generality in the sparse structure. Essentially, ICR solves a sequence of convex optimization problems such that sequence of solutions converges to a sub-optimal solution of the original hard optimization problem. We propose two versions of our algorithm: a.) an unconstrained version, and b.) with a non-negativity constraint on sparse coefficients, which may be required in some real-world problems. Experimental validation is performed on both synthetic data and for a real-world image recovery problem, which illustrates merits of ICR over state of the art alternatives.

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Hierarchical sparse modeling using Spike and Slab priors

Cited by 11 publications

References 17 publications

A Hierarchical Spike-and-Slab Model for Pan-Cancer Survival Using Pan-Omic Data

A Hierarchical Spike-and-Slab Model for Pan-Cancer Survival Using Pan-Omic Data

Multi-task image classification via collaborative, hierarchical spike-and-slab priors

Iterative Convex Refinement for Sparse Recovery

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