Cluster Quilting: Spectral Clustering for Patchwork Learning

TL;DR

This paper introduces Cluster Quilting, a spectral clustering method designed for patchwork data where samples and features are observed in fragmented subsets, enabling comprehensive clustering analysis despite incomplete data overlaps.

Contribution

The paper presents a novel spectral clustering algorithm tailored for patchwork learning, with theoretical guarantees and superior empirical performance in neuroscience and genomics.

Findings

Achieves accurate clustering under sub-Gaussian mixture models.

Outperforms existing methods in simulated and real datasets.

Provides theoretical bounds on misclustering rates.

Abstract

Patchwork learning arises as a new and challenging data collection paradigm where both samples and features are observed in fragmented subsets. Due to technological limits, measurement expense, or multimodal data integration, such patchwork data structures are frequently seen in neuroscience, healthcare, and genomics, among others. Instead of analyzing each data patch separately, it is highly desirable to extract comprehensive knowledge from the whole data set. In this work, we focus on the clustering problem in patchwork learning, aiming at discovering clusters amongst all samples even when some are never jointly observed for any feature. We propose a novel spectral clustering method called Cluster Quilting, consisting of (i) patch ordering that exploits the overlapping structure amongst all patches, (ii) patchwise SVD, (iii) sequential linear mapping of top singular vectors for patch overlaps, followed by (iv) k-means on the combined and weighted singular vectors. Under a sub-Gaussian mixture model, we establish theoretical guarantees via a non-asymptotic misclustering rate bound that reflects both properties of the patch-wise observation regime as well as the clustering signal and noise dependencies. We also validate our Cluster Quilting algorithm through extensive empirical studies on both simulated and real data sets in neuroscience and genomics, where it discovers more accurate and scientifically more plausible clusters than other approaches.