Patchwork learning arises as a new and challenging data collection paradigm where both samples and features are observed in fragmented subsets. Due to technological limitations and measurement expenses, such patchwork data structures are frequently seen in applications like neuroscience, healthcare, and genomics, among others. Instead of analyzing each data patch separately, it is highly desirable to extract comprehensive knowledge from the whole dataset. In this work, we focus on the clustering problem in patchwork learning, aiming at discovering clusters among 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) patch-wise SVD, (iii) sequential linear mapping of top singular vectors for patch overlaps, followed by (iv) k-means on the combined and weighted singular vectors. 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 validate our Cluster Quilting algorithm through empirical studies on both simulated and real datasets, where we show Cluster Quilting yields more accurate and scientifically more plausible clusters than other approaches. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
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