原摘要: Multi-view clustering aims to employ semantic information from multiple perspectives to accomplish the clustering task. However, a crucial concern in this domain is the selection of distinctive features. Most existing methods map data into a single feature space and then construct a similarity matrix, which often leads to an insufficient utilisation of intrinsic information in the data, meanwhile neglecting the impact of noise in the data, resulting in poor representation learning performance. Information bottleneck (IB) is a theoretical model based on information theory, the core idea of which is to extract information that is useful for a given task by selecting an appropriate representation and discarding redundant and irrelevant information. In this study, we propose an innovative IB fusion model for deep multi-view clustering (IBFDMVC), which operates on two distinct feature spaces and reconstructs semantic information in a parallel manner. IBFDMVC consists of three modules. The encoder module uses two linear encoding layers to learn and obtain embeddings with different dimensions. The fusion module adopts a collaborative training learning concept, where contrastive learning is first employed to enhance representation and IB theory is further used to reduce representation noise. Finally, clustering is performed using k-means in the clustering module. Compared with state-of-the-art multi-view clustering methods, IBFDMVC achieves better results, verifying the significant role of IB theory in providing a robust framework for feature selection and semantic information extraction in multi-view data analysis.
七步分如下(每一句都要有翻译):
-
交代背景:
- "Multi-view clustering aims to employ semantic information from multiple perspectives to accomplish the clustering task."
- 多视图聚类旨在利用来自多个视角的语义信息来完成聚类任务。
-
概括当前方法:
- "Most existing methods map data into a single feature space and then construct a similarity matrix."
- 大多数现有方法将数据映射到单一特征空间，然后构建相似性矩阵。
-
现有方法的不足:
- "This often leads to an insufficient utilisation of intrinsic information in the data, meanwhile neglecting the impact of noise in the data, resulting in poor representation learning performance."
- 这通常导致对数据中内在信息的利用不足，同时忽略了数据中噪声的影响，导致表示学习性能差。
-
提出当前的方法:
- "We propose an innovative IB fusion model for deep multi-view clustering (IBFDMVC)."
- 我们提出了一种创新的深度多视图聚类信息瓶颈融合模型（IBFDMVC）。
-
简要介绍方法:
- "IBFDMVC operates on two distinct feature spaces and reconstructs semantic information in a parallel manner."
- IBFDMVC在两个不同的特征空间上操作，并以并行方式重构语义信息。
-
如何实现或优化:
- "The fusion module adopts a collaborative training learning concept, where contrastive learning is first employed to enhance representation and IB theory is further used to reduce representation noise."
- 融合模块采用了协作训练学习概念，首先使用对比学习增强表示，然后进一步使用信息瓶颈理论减少表示噪声。
-
实验介绍:
- "Compared with state-of-the-art multi-view clustering methods, IBFDMVC achieves better results."
- 与最先进的多视图聚类方法相比，IBFDMVC取得了更好的结果。
总结: 本研究提出了一种基于信息瓶颈理论的深度多视图聚类新模型（IBFDMVC），该模型通过在两个独特的特征空间上操作并以并行方式重构语义信息来解决传统多视图聚类方法中特征选择不足和忽略数据噪声的问题。通过采用对比学习和信息瓶颈理论相结合的融合模块，IBFDMVC有效地增强了数据表示并减少了表示噪声，最终通过k-means聚类模块完成聚类任务
