Learning the Precise Feature for Cluster Assignment

by   Yanhai Gan, et al.

Clustering is one of the fundamental tasks in computer vision and pattern recognition. Recently, deep clustering methods (algorithms based on deep learning) have attracted wide attention with their impressive performance. Most of these algorithms combine deep unsupervised representation learning and standard clustering together. However, the separation of representation learning and clustering will lead to suboptimal solutions because the two-stage strategy prevents representation learning from adapting to subsequent tasks (e.g., clustering according to specific cues). To overcome this issue, efforts have been made in the dynamic adaption of representation and cluster assignment, whereas current state-of-the-art methods suffer from heuristically constructed objectives with representation and cluster assignment alternatively optimized. To further standardize the clustering problem, we audaciously formulate the objective of clustering as finding a precise feature as the cue for cluster assignment. Based on this, we propose a general-purpose deep clustering framework which radically integrates representation learning and clustering into a single pipeline for the first time. The proposed framework exploits the powerful ability of recently developed generative models for learning intrinsic features, and imposes an entropy minimization on the distribution of the cluster assignment by a dedicated variational algorithm. Experimental results show that the performance of the proposed method is superior, or at least comparable to, the state-of-the-art methods on the handwritten digit recognition, fashion recognition, face recognition and object recognition benchmark datasets.


page 1

page 4

page 10

page 14


Deep adaptive fuzzy clustering for evolutionary unsupervised representation learning

Cluster assignment of large and complex images is a crucial but challeng...

Consensus Clustering with Unsupervised Representation Learning

Recent advances in deep clustering and unsupervised representation learn...

Representation Learning for Clustering via Building Consensus

In this paper, we focus on deep clustering and unsupervised representati...

Self-Evolutionary Clustering

Deep clustering outperforms conventional clustering by mutually promotin...

Improved Representation Learning Through Tensorized Autoencoders

The central question in representation learning is what constitutes a go...

RepBin: Constraint-based Graph Representation Learning for Metagenomic Binning

Mixed communities of organisms are found in many environments (from the ...

Discovering Traveling Companions using Autoencoders

With the wide adoption of mobile devices, today's location tracking syst...

Please sign up or login with your details

Forgot password? Click here to reset