Multi-View Fusion with Extreme Learning Machine for Clustering
Multi-View Fusion with Extreme Learning Machine for Clustering
复制标题
DOI：
10.1145/3340268
复制
发表时间：
2019-10
期刊：
影响因子：
--
通讯作者：
Yongshan Zhang;Jia Wu;Chuan Zhou;Z. Cai;Jian Yang-;Philip S. Yu
中科院分区：
文献类型：
--
作者：
Yongshan Zhang;Jia Wu;Chuan Zhou;Z. Cai;Jian Yang-;Philip S. Yu
未标记的多视图数据在许多现实世界的数据分析任务中提出了相当大的挑战。这些数据值得探索，因为它们通常包含补充信息，可以提高分析结果的质量。多视图数据的聚类是一个特别具有挑战性的问题，因为揭示许多特征空间之间的复杂数据结构需要特定于任务的区分特征，并且当这些特征太少时，性能会受到影响。极限学习机是一种新兴的学习模型，它在一系列不同的学习任务中表现出出色的表示能力和上级性能。出于这一进步的承诺，我们已经开发出一种新的多视图融合聚类框架的基础上ELM，称为MVEC。MVEC通过ELM网络从数据的每个视图中学习嵌入，然后根据每个嵌入之间的相关性和依赖性构建单个统一的嵌入，并自动加权每个嵌入的贡献。这个过程以高度的准确性暴露了嵌入在多视图数据中的底层聚类结构。一个简单而有效的解决方案也提供了解决MVEC内的优化问题。在不同领域的八个不同基准上的实验和比较证实了MVEC的聚类准确性。
Unlabeled, multi-view data presents a considerable challenge in many real-world data analysis tasks. These data are worth exploring because they often contain complementary information that improves the quality of the analysis results. Clustering with multi-view data is a particularly challenging problem as revealing the complex data structures between many feature spaces demands discriminative features that are specific to the task and, when too few of these features are present, performance suffers. Extreme learning machines (ELMs) are an emerging form of learning model that have shown an outstanding representation ability and superior performance in a range of different learning tasks. Motivated by the promise of this advancement, we have developed a novel multi-view fusion clustering framework based on an ELM, called MVEC. MVEC learns the embeddings from each view of the data via the ELM network, then constructs a single unified embedding according to the correlations and dependencies between each embedding and automatically weighting the contribution of each. This process exposes the underlying clustering structures embedded within multi-view data with a high degree of accuracy. A simple yet efficient solution is also provided to solve the optimization problem within MVEC. Experiments and comparisons on eight different benchmarks from different domains confirm MVEC’s clustering accuracy.