Sparse Convex Biclustering

TL;DR

This paper introduces SpaCoBi, a convex biclustering method that enhances accuracy and robustness in high-dimensional data by penalizing noise and employing a stability-based tuning criterion.

Contribution

SpaCoBi is a novel convex biclustering approach that improves accuracy and stability in large-scale, high-dimensional datasets through noise penalization and a new tuning method.

Findings

SpaCoBi outperforms existing methods in accuracy.

It demonstrates robustness in large-scale datasets.

Effective in high-dimensional omics data.

Abstract

Biclustering is an essential unsupervised machine learning technique for simultaneously clustering rows and columns of a data matrix, with widespread applications in genomics, transcriptomics, and other high-dimensional omics data. Despite its importance, existing biclustering methods struggle to meet the demands of modern large-scale datasets. The challenges stem from the accumulation of noise in high-dimensional features, the limitations of non-convex optimization formulations, and the computational complexity of identifying meaningful biclusters. These issues often result in reduced accuracy and stability as the size of the dataset increases. To overcome these challenges, we propose Sparse Convex Biclustering (SpaCoBi), a novel method that penalizes noise during the biclustering process to improve both accuracy and robustness. By adopting a convex optimization framework and introducing a stability-based tuning criterion, SpaCoBi achieves an optimal balance between cluster fidelity and sparsity. Comprehensive numerical studies, including simulations and an application to mouse olfactory bulb data, demonstrate that SpaCoBi significantly outperforms state-of-the-art methods in accuracy. These results highlight SpaCoBi as a robust and efficient solution for biclustering in high-dimensional and large-scale datasets.