Summary of Evolving Restricted Boltzmann Machine-kohonen Network For Online Clustering, by J. Senthilnath et al.
Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering
by J. Senthilnath, Adithya Bhattiprolu, Ankur Singh, Bangjian Zhou, Min Wu, Jón Atli Benediktsson, Xiaoli Li
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel online clustering algorithm combines an Evolving Restricted Boltzmann Machine (ERBM) with a Kohonen Network, called ERBM-KNet. The ERBM-KNet efficiently handles streaming data in a single-pass mode using the ERBM’s bias-variance strategy for neuron growing and pruning, as well as online clustering based on a cluster update strategy for cluster prediction and cluster center update using KNet. Initially, the ERBM evolves its architecture while processing unlabeled image data, effectively disentangling the data distribution in the latent space. Subsequently, the KNet utilizes the feature extracted from ERBM to predict the number of clusters and updates the cluster centers. This approach overcomes common challenges associated with clustering algorithms, such as prior initialization of the number of clusters and subpar clustering accuracy, offering significant improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to group similar things together using computer programs. It combines two ideas: an “evolving restricted Boltzmann machine” that gets better at recognizing patterns in data, and a “Kohonen network” that helps find the right number of groups. This combination makes it possible to process data as it comes in, without needing to know how many groups there will be beforehand. The new method is tested on several datasets and does better than other methods. |
Keywords
* Artificial intelligence * Clustering * Latent space * Pruning
