TFMKC: Tuning-Free Multiple Kernel Clustering Coupled With Diverse Partition Fusion

Junpu Zhang, Liang Li

Abstract:

Clustering is a popular research pipeline in unsupervised
learning to find potential groupings. As a representative
paradigm in multiple kernel clustering (MKC), late fusion-based
models learn a consistent partition across multiple base kernels.
Despite their promising performance, a common concern is the
limited representation capacity caused by the inflexible fusion
mechanism. Concretely, the representations are constrained by
truncated-k Eigen-decomposition (EVD) without fully exploiting
potential information. An intuitive idea to alleviate this concern
is to generate a set of augmented partitions and then select the
optimal partition by fine-tuning. However, this is overlimited by:
1) introducing undesired hyperparameters and dataset-related
consequences; 2) neglecting rich information across diverse partitions;
and 3) expensive parameter-tuning costs. To address these
problems, we propose transforming the challenging problem of
directly determining the optimal partition (optimal parameter)
into a diverse partition fusion (parameter ensemble) problem.
We design a novel flexible fusion mechanism called tuning-free
multiple kernel clustering coupled with diverse partition fusion
(TFMKC) by reweighting diverse partitions through optimization,
achieving an optimal consensus partition by integrating
diverse and complementary information rather than traditional
fine-tuning, and distinguishing our work from existing methods.
Extensive experiments verify that TFMKC achieves competitive
effectiveness and efficiency over comparison baselines. The code
can be accessed at https://github.com/ZJP/TFMKC.