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[Page no. 3] "to generate view-specific features, and weighted fusion is em- ployed to generate the fusion view features. The cross-view double-level con- trastive fusion module employs two consistency objectives: semantics-guided instance-level contrastive learning and cluster-level contrastive learning. Instance pair weights measured by semantic features are applied to mitigate the impact of false negative pairs. Furthermore, in order to reduce the influence of view-private information, common information are concentrated when calculating instance pair weights. Compared with previous work, our contributions are as follows: • A DCMCS framework is proposed to lessen the impact of false negative pairs in instance-level contrastive learning. The objective of contrastive learning is made consistent with the clustering objective by using instance pair weights obtained from semantic features to learn cluster-friendly features. • When calculating the instance pair weights, the common information of the fusion view are concentrated in DCMCS. It relieves the interference of the view-private information in the fusion view to achieve better clustering re- sults. • In the experiment, we show how effectively the instance pair weights and the fusion view’s common information work. Experimental results on several public datasets demonstrate our framework outperforms the state-of-the-art methods. The rest of the paper is organized as follows. The relevant work is presented in Sec. 2. The proposed framework is presented in Sec. 3. Comprehensive exper-  Deep Contrastive Multi-view Clustering under Semantic Feature Guidance 3 iments and findings are reported in Sec. 4. Sec. 5 provides the conclusion and future work. 2 Related Work 2.1 Multi-view clustering. With the development of deep learning, deep MVC has an important position in MVC methods. The four categories of deep MVC methods are deep repre- sentation learning [8, 26, 34, 45], deep graph learning [23, 33, 38], subspace clus- tering [22, 30, 31], and spectral clustering [3, 27, 49]; the"

[Page no. 1] "Deep Contrastive Multi-view Clustering under Semantic Feature Guidance Siwen Liu1, Jinyan Liu1, Hanning Yuan1, Qi Li1, Jing Geng1, Ziqiang Yuan1, and Huaxu Han1 Beijing Institute of technology, China Abstract. Contrastive learning has achieved promising performance in the field of multi-view clustering recently. However, the positive and negative sample construction mechanisms ignoring semantic consistency lead to false negative pairs, limiting the performance of existing algo- rithms from further improvement. To solve this problem, we propose a multi-view clustering framework named Deep Contrastive Multi-view Clustering under Semantic feature guidance (DCMCS) to alleviate the influence of false negative pairs. Specifically, view-specific features are firstly extracted from raw features and fused to obtain fusion view fea- tures according to view importance. To mitigate the interference of view- private information, specific view and fusion view semantic features are learned by cluster-level contrastive learning and concatenated to measure the semantic similarity of instances. By minimizing instance-level con- trastive loss weighted by semantic similarity, DCMCS adaptively weak- ens contrastive leaning between false negative pairs. Experimental results on several public datasets demonstrate the proposed framework outper- forms the state-of-the-art methods. Keywords: Multi-view clustering · Contrastive learning · False negative pairs · Semantic feature 1 Introduction Multi-view clustering(MVC) has gained interest in the last few years and has been widely applied in the field of biology [48], medicine [7, 44], social net- work [47], agriculture [32], and so forth. Traditional methods for MVC include non-negative matrix factorization [46,52], latent representation learning [17,39], graph learning [18,37], and tensor learning [2,29]. Many of the traditional MVC methods suffer from poor representation and high computational complexity, resulting in limited performance in complex scenarios with real data [12]. Deep learning develops superior features with its strong representation ability, so deep MVC is becoming popular. Deep MVC methods can be categorized into four sub-"

[Page no. 11] "0.4516 0.5757 0.5914 0.4981 0.5914 0.5571 0.5061 0.5735 0.7207 0.6418 0.7221 SiMVC (2021) 0.5083 0.4715 0.5573 0.5692 0.4953 0.5912 0.6193 0.5362 0.6303 0.7193 0.6771 0.7292 CoMVC (2021) 0.4663 0.4262 0.5272 0.5413 0.5043 0.5842 0.5683 0.5692 0.6463 0.7003 0.6871 0.7462 MFLVC (2022) 0.6060 0.5280 0.6160 0.6312 0.5663 0.6392 0.7332 0.6523 0.7342 0.8042 0.7032 0.8043 CVCL (2023) 0.6479 0.5503 0.6479 0.6664 0.5493 0.6664 0.6643 0.5934 0.6864 0.7457 0.6549 0.5903 AECoDDC (2023) 0.4579 0.3410 0.4607 0.5857 0.4549 0.6057 0.4893 0.3923 0.5271 0.6564 0.5898 0.6786 GCFAgg (2023) 0.6643 0.5008 0.6643 0.6400 0.5345 0.6529 0.7343 0.6610 0.7343 0.8336 0.7331 0.8336 DCMCS(ours) 0.6664 0.5709 0.6664 0.7543 0.6582 0.7600 0.8464 0.7291 0.8464 0.8907 0.8172 0.8907 Each view feature includes common and meaningless view-private information [41]. The importance of each view is taken into consideration by the fusion view. However, the issue of view fusion-related representational deterioration can also affect the clustering results [40]. We use weighted fusion to alleviate the representation degradation problem and focus on the common information of the fused views to further alleviate the interference of view-private information. We further analyze DCMCS’s performance on datasets with varying numbers of views using the Caltech dataset. The results in Tab. 4 indicate that (1) The efficiency of our approach gets better as the number of views rises. It proves that DCMCS is robust on datasets with different number of views. (2) Compared to the best method, GCFAgg, DCMCS increases ACC metric on the Caltech-4V dataset by around 13%. The quality of the Caltech dataset varies depending on the view, as seen in [40]. The semantic features we adopted are more useful when there are significant variations across views. (3) Compared to the methods MFLVC and CVCL which do not use fusion, our method improves ACC about 16% and 11% on Caltech-3V respectively, proving considering the importance"

[Page no. 12] "of the different views is important. In Fig. 3, the visualization of GCFAgg Fig. 3a-Fig. 3c and our DCMCS Fig. 3d-Fig. 3f are achieved through the application of t-SNE on the two views of Hdigit and the fusion view. Our approach, in contrast to GCFAgg, enhances the clustering effect of views, resulting in a more compact intra-cluster structure and farther-separating inter-clusters. This is due to the fact that we introduce instance pair weights using semantic features and take into account cluster-level contrastive learning. 4.3 Ablation Studies Loss components. We use two datasets with distinct properties for the loss function ablation studies. Caltech-5V is a tiny dataset with varying view quality; Hdigit is large, and the two views’ clustering accuracy is very little different, at 0.9420 and 0.9637. Tab. 5 displays the optimal results for both datasets that in- corporate all loss functions. In case (a), we apply k-means [1] on the ˆZ to achieve  12 Liu et al. (a) View 1 (b) View 2 (c) Fusion view (d) View 1 (e) View 2 (f) Fusion view Fig. 3: Visualization of GCFAgg (a-c) and DCMCS (d-f) on Hdigit’s two views and fusion view Table 5: Ablation studies on loss components Components Caltech-5V Hdigit Lcon Li Lc ACC NMI ACC NMI (a) ✓ 0.8850 0.7957 0.9387 0.8636 (b) ✓ ✓ 0.4357 0.4387 0.9525 0.9091 (c) ✓ ✓ 0.7157 0.6566 0.9919 0.9753 (d) ✓ ✓ ✓ 0.8907 0.8172 0.9940 0.9811 (e) ✓ ✓ 0.6657 0.5661 0.9933 0.9791 clustering results. The findings show that improved features can be learned in the reconstruction. We use k-means on the ˆH in (b) to obtain clustering results. In comparison to (a), it improves on Hdigit but drastically degrades on Caltech- 5V. When there is an adequate number of instances, instance-level contrastive learning is more helpful. In"

[Page no. 3] "latter three are fre- quently combined [9,24,43]. In deep representation learning, contrastive meth- ods [15,34,41], collaborative methods [45,51], adversarial methods [26,50], and distillation methods [8, 28] can be used to acquire common information. Yang et al. [45] optimize view features using intra-view collaborative learning and gain complementing information through inter-view collaborative learning. Li et al. [26] employ adversarial learning to further capture data distribution. A dis- tillation approach is presented by Chen et al. [8] that uses a teacher model with complete views to direct a student model with lacking views. Contrastive learning is frequently employed in deep MVC since it supports the clustering objective. We present a semantics-guided contrastive multi-view clustering framework that takes advantage of the consistency and complementarity of views. 2.2 Contrastive learning. Contrastive learning [6, 14] has proven to be quite effective in self-supervised learning in recent years. By creating positive and negative pairs, contrastive learning is able to obtain discriminative features. New perspectives on the diffi- culties of deep clustering are offered by the development of contrastive cluster- ing [25]. In general, there are two types of multi-view contrastive clustering: instance-level contrastive learning [34, 42] and cluster-level contrastive learn- ing [4]. [15,41] make use of both. False negative pairs occur when setting negative pairs in the popular instance-level contrastive learning technique. In order to mitigate this issue, Trosten et al. [34] use negative pairs consisting of instances assigned to distinct clusters, whereas Yan et al. [42] add structure relationship to negative pairs. Semantics-guided instance-level contrastive learning and cluster- level contrastive learning are utilized in our framework. To reduce false negative pairs, instance pair weights derived from semantic features are introduced. 3 Method In this section, we first explain the definitions used and the goal to be achieved in the framework. Next, we introduce the architecture"

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