# Incremental cluster validity index-guided online learning for performance and robustness to presentation order

arXiv: https://arxiv.org/abs/2108.07743 · HTML: https://mlsanity.com/paper/2108.07743/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 113 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 17 of 17)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [Deep Cosine Metric Learning for Person Re-Identification](https://mlsanity.com/paper/1812.00442/) (2018)
- [Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations](https://mlsanity.com/paper/1710.01494/) (2017)
- [Data Stream Clustering: A Review](https://mlsanity.com/paper/2007.10781/) (2020)
- [Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence](https://mlsanity.com/paper/1901.00794/) (2018)
- [A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications](https://mlsanity.com/paper/1905.11437/) (2019)
- [Uncertainty-based Modulation for Lifelong Learning](https://mlsanity.com/paper/2001.09822/) (2020)
- [StreamSoNG: A Soft Streaming Classification Approach](https://mlsanity.com/paper/2010.00635/) (2020)
- [Online Cluster Validity Indices for Streaming Data](https://mlsanity.com/paper/1801.02937/) (2018)
- [s-DRN: Stabilized Developmental Resonance Network](https://mlsanity.com/paper/1912.08541/) (2019)
- [iCVI-ARTMAP: Accelerating and improving clustering using adaptive resonance theory predictive mapping and incremental cluster validity indices](https://mlsanity.com/paper/2008.09903/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning](https://mlsanity.com/paper/2305.01507/) (2023)
- [A correlation-based fuzzy cluster validity index with secondary options detector](https://mlsanity.com/paper/2308.14785/) (2023)
- [Towards Initialization-Agnostic Clustering with Iterative Adaptive Resonance Theory](https://mlsanity.com/paper/2505.04440/) (2025)

## Abstract (arXiv)

In streaming data applications incoming samples are processed and discarded, therefore, intelligent decision-making is crucial for the performance of lifelong learning systems. In addition, the order in which samples arrive may heavily affect the performance of online (and offline) incremental learners. The recently introduced incremental cluster validity indices (iCVIs) provide valuable aid in addressing such class of problems. Their primary use-case has been cluster quality monitoring; nonetheless, they have been very recently integrated in a streaming clustering method to assist the clustering task itself. In this context, the work presented here introduces the first adaptive resonance theory (ART)-based model that uses iCVIs for unsupervised and semi-supervised online learning. Moreover, it shows for the first time how to use iCVIs to regulate ART vigilance via an iCVI-based match tracking mechanism. The model achieves improved accuracy and robustness to ordering effects by integrating an online iCVI framework as module B of a topological adaptive resonance theory predictive mapping (TopoARTMAP) -- thereby being named iCVI-TopoARTMAP -- and by employing iCVI-driven post-processing heuristics at the end of each learning step. The online iCVI framework provides assignments of input samples to clusters at each iteration in accordance to any of several iCVIs. The iCVI-TopoARTMAP maintains useful properties shared by ARTMAP models, such as stability, immunity to catastrophic forgetting, and the many-to-one mapping capability via the map field module. The performance (unsupervised and semi-supervised) and robustness to presentation order (unsupervised) of iCVI-TopoARTMAP were evaluated via experiments with a synthetic data set and deep embeddings of a real-world face image data set.