Multiple clustering has gained significant attention in recent years due to
its potential to reveal multiple hidden structures of data from different
perspectives. The advent of deep multiple clustering techniques has notably
advanced the performance by uncovering complex patterns and relationships
within large datasets. However, a major challenge arises as users often do not
need all the clusterings that algorithms generate, and figuring out the one
needed requires a substantial understanding of each clustering result.
Traditionally, aligning a user's brief keyword of interest with the
corresponding vision components was challenging, but the emergence of
multi-modal and large language models (LLMs) has begun to bridge this gap. In
response, given unlabeled target visual data, we propose Multi-MaP, a novel
method employing a multi-modal proxy learning process. It leverages CLIP
encoders to extract coherent text and image embeddings, with GPT-4 integrating
users' interests to formulate effective textual contexts. Moreover, reference
word constraint and concept-level constraint are designed to learn the optimal
text proxy according to the user's interest. Multi-MaP not only adeptly
captures a user's interest via a keyword but also facilitates identifying
relevant clusterings. Our extensive experiments show that Multi-MaP
consistently outperforms state-of-the-art methods in all benchmark
multi-clustering vision tasks. Our code is available at
this https URL

给定未标记的目标视觉数据，作者提出了一种名为 Multi-MaP 的新方法，它利用多模态代理学习过程，借助 CLIP 编码器提取一致的文本和图像嵌入，GPT-4 整合用户的兴趣来制定有效的文本上下文，并设计了参考词约束和概念级约束以根据用户的兴趣学习最佳的文本代理。该方法不仅能够通过关键词熟练地捕捉用户的兴趣，而且还有助于识别相关的聚类，实验证明 Multi-MaP 在所有基准多聚类视觉任务中始终优于最先进的方法。

面向个性化视觉多重聚类的多模态代理学习

Multi-Modal Proxy Learning Towards Personalized Visual Multiple  Clustering

Multiple clustering has gathered significant attention in recent years due to
its potential to reveal multiple hidden structures of the data from different
perspectives. Most of multiple clustering methods first derive feature
representations by controlling the dissimilarity among them, subsequently
employing traditional clustering methods (e.g., k-means) to achieve the final
multiple clustering outcomes. However, the learned feature representations can
exhibit a weak relevance to the ultimate goal of distinct clustering. Moreover,
these features are often not explicitly learned for the purpose of clustering.
Therefore, in this paper, we propose a novel Dual-Disentangled deep Multiple
Clustering method named DDMC by learning disentangled representations.
Specifically, DDMC is achieved by a variational Expectation-Maximization (EM)
framework. In the E-step, the disentanglement learning module employs
coarse-grained and fine-grained disentangled representations to obtain a more
diverse set of latent factors from the data. In the M-step, the cluster
assignment module utilizes a cluster objective function to augment the
effectiveness of the cluster output. Our extensive experiments demonstrate that
DDMC consistently outperforms state-of-the-art methods across seven commonly
used tasks. Our code is available at this https URL

本研究提出了一种名为 DDMC 的新型双解缠绕深度多聚类方法，通过学习解缠绕表示来实现多个隐藏数据结构的独立聚类目标，并通过变分期望最大化（EM）框架进行实现，实验证明 DDMC 在七个常用任务上始终优于现有的方法。

双解离深度多聚类

Dual-disentangled Deep Multiple Clustering

Multiple clustering aims at exploring alternative clusterings to organize the
data into meaningful groups from different perspectives. Existing multiple
clustering algorithms are designed for single-view data. We assume that the
individuality and commonality of multi-view data can be leveraged to generate
high-quality and diverse clusterings. To this end, we propose a novel
multi-view multiple clustering (MVMC) algorithm. MVMC first adapts multi-view
self-representation learning to explore the individuality encoding matrices and
the shared commonality matrix of multi-view data. It additionally reduces the
redundancy (i.e., enhancing the individuality) among the matrices using the
Hilbert-Schmidt Independence Criterion (HSIC), and collects shared information
by forcing the shared matrix to be smooth across all views. It then uses matrix
factorization on the individual matrices, along with the shared matrix, to
generate diverse clusterings of high-quality. We further extend multiple
co-clustering on multi-view data and propose a solution called multi-view
multiple co-clustering (MVMCC). Our empirical study shows that MVMC (MVMCC) can
exploit multi-view data to generate multiple high-quality and diverse
clusterings (co-clusterings), with superior performance to the state-of-the-art
methods.

本研究介绍了一种名为 MVMC 的多视图多聚类算法，将自表示学习应用于多视图数据中，使用 Hilbert-Schmidt 独立性标准降低矩阵之间的冗余并收集共享信息，再采用矩阵分解生成多个高质量多样性聚类，并进一步扩展到多视图多共聚类（MVMCC）。实验结果表明，本方法在多视图数据中生成的多样性聚类（共聚类）优于当前现有的算法。