PC-X: Profound Clustering via Slow Exemplars
Keywords: Deep clustering, interpretable machine learning, Optimization
TL;DR: In this paper, we design a new end-to-end framework called Profound Clustering via slow eXemplars (PC-X), which is inherent interpretable and universally applicable to various types of large-scale datasets.
Abstract: Deep clustering aims at learning clustering and data representation jointly to deliver clustering-friendly representation. In spite of their significant improvements in clustering accuracy, existing approaches are far from meeting the requirements from other perspectives, such as universality, interpretability and efficiency, which become increasingly important with the emerging demand for diverse applications. We introduce a new framework named Profound Clustering via slow eXemplars (PC-X), which fulfils the above four basic requirements simultaneously.
In particular, PC-X encodes data within the auto-encoder (AE) network to reduce its dependence on data modality (\textit{universality}).
Further, inspired by exemplar-based clustering, we design a \PCX{Centroid-Integration Unit (CI-Unit)}, which not only facilitate the suppression of sample-specific details for better representation learning (\textit{accuracy}), but also prompt clustering centroids to become legible exemplars (\textit{interpretability}). Further, these exemplars are calibrated stably with mini-batch data following our tailor-designed optimization scheme and converges in linear (\textit{efficiency}). Empirical results on benchmark datasets demonstrate the superiority of PC-X in terms of universality, interpretability and efficiency, in addition to clustering accuracy. The code of this work is available at https://github.com/Yuangang-Pan/PC-X/.
Track Confirmation: Yes, I am submitting to the proceeding track.
Submission Number: 38
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