Boosting Spectral Clustering on Incomplete Data via Kernel Correction and Affinity Learning

Published: 21 Sept 2023, Last Modified: 20 Dec 2023NeurIPS 2023 posterEveryoneRevisionsBibTeX
Keywords: Spectral Clustering, Incomplete Data, Kernel Correction, Self-expressive Affinity Learning
TL;DR: The paper proposes two new approaches to improve spectral clustering on incomplete data using kernel correction and affinity learning.
Abstract: Spectral clustering has gained popularity for clustering non-convex data due to its simplicity and effectiveness. It is essential to construct a similarity graph using a high-quality affinity measure that models the local neighborhood relations among the data samples. However, incomplete data can lead to inaccurate affinity measures, resulting in degraded clustering performance. To address these issues, we propose an imputation-free framework with two novel approaches to improve spectral clustering on incomplete data. Firstly, we introduce a new kernel correction method that enhances the quality of the kernel matrix estimated on incomplete data with a theoretical guarantee, benefiting classical spectral clustering on pre-defined kernels. Secondly, we develop a series of affinity learning methods that equip the self-expressive framework with $\ell_p$-norm to construct an intrinsic affinity matrix with an adaptive extension. Our methods outperform existing data imputation and distance calibration techniques on benchmark datasets, offering a promising solution to spectral clustering on incomplete data in various real-world applications.
Submission Number: 4702