Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning

Abstract

Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand. This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points. In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm. Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms.

Class-Wise Classifier Design Capable of Continual Learning Using Adaptive Resonance Theory-Based Topological Clustering

“…One drawback of the ART-based algorithms is the specification of data-dependent parameters such as a similarity threshold. Several studies have proposed solving this drawback by utilizing multiple vigilance levels [31], adjusting parameters during a learning process [24], and estimating parameters from given data [16]. In particular, CAEA [16], which utilizes the CIM as a similarity measure, has shown superior clustering performance while successfully reducing the effect of data-dependent parameters.…”

“…CAEA [16] is an ART-based topological clustering algorithm capable of continual learning. In [16], CAEA and its hierarchical approach show comparable clustering performance to recently-proposed clustering algorithms without the difficulty of parameter specifications to each dataset. The learning procedure of CAEA is divided into four parts:…”

“…Algorithm 2: Learning Algorithm of CAEA [16] Input: a set of training data points: X = {x 1 , x 2 , . .…”

“…Among recent ART-based clustering algorithms, an algorithm that utilizes Correntropy-Induced Metric (CIM) [12] as a similarity measure shows faster and more stable self-organizing performance than GNG-based algorithms [13][14][15]. We apply CIMbased ART with Edge and Age (CAEA) [16] as a base clustering algorithm in the proposed algorithm. Moreover, we also propose two variants of the proposed algorithm by modifying a computation of the CIM to improve the classification performance.…”

“…a (k 1 ,j) > a max then V threshold then16 Initialize a counter M K+1 = 1. 17 Update a set of counter M ← M ∪ M K+1 .18Create the new node as y K+1 = x l .19Calculate the kernel bandwidth σ k+1 by (8) and(9).…”