用于在线聚类的演化受限玻尔兹曼机-Kohonen网络
Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering
AI总结：
提出ERBM-KNet在线聚类算法，结合演化受限玻尔兹曼机与Kohonen网络，通过单遍模式处理流数据，自动预测聚类数并更新中心，显著提升聚类精度。
摘要：
本文提出了一种新型在线聚类算法，其中演化受限玻尔兹曼机（ERBM）嵌入到一个Kohonen网络中，称为ERBM-KNet。所提出的ERBM-KNet使用ERBM以单遍模式高效处理流数据，采用偏差-方差策略进行神经元生长和剪枝，以及基于聚类更新策略的在线聚类，用于聚类预测和使用KNet进行聚类中心更新。最初，ERBM在处理未标记图像数据时演化其架构，有效地解开潜在空间中的数据分布。随后，KNet利用从ERBM提取的特征来预测聚类数量并更新聚类中心。通过克服与聚类算法相关的常见挑战，例如聚类数量的先验初始化和较差的聚类精度，所提出的ERBM-KNet提供了显著的改进。在四个基准和一个工业数据集上的广泛实验评估证明了ERBM-KNet相比最先进方法的优越性。
英文摘要：
A novel online clustering algorithm is presented where an Evolving Restricted Boltzmann Machine (ERBM) is embedded with a Kohonen Network called ERBM-KNet. The proposed ERBM-KNet efficiently handles streaming data in a single-pass mode using the ERBM, employing a 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, 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. By overcoming the common challenges associated with clustering algorithms, such as prior initialization of the number of clusters and subpar clustering accuracy, the proposed ERBM-KNet offers significant improvements. Extensive experimental evaluations on four benchmarks and one industry dataset demonstrate the superiority of ERBM-KNet compared to state-of-the-art approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
