Multimodal learning has achieved remarkable success by integrating heterogeneous information from multiple sources. However, most existing methods either treat all samples uniformly or rely on deterministic prediction correctness to guide cross-modal alignment, overlooking the varying degrees of epistemic uncertainty inherent in each modality. Such assumptions often lead to noise propagation, especially when models exhibit over-confident yet unreliable predictions. To address this limitation, we propose Adaptive Predictive Contrastive Learning (AdaPCL), an evidential uncertainty modulated framework designed to regulate cross-modal interactions based on modality-specific reliability. Specifically, we first employ evidential deep learning to explicitly quantify modality-specific epistemic uncertainty, establishing a dual-view assessment that calibrates discriminative confidence with evidential reliability. Building upon this, we introduce a continuous reliability-modulated mechanism that synthesizes these dual perspectives to assign soft, instance-specific weights. This design enables AdaPCL to formulate a unified and adaptive contrastive objective encompassing three complementary alignment strategies: (i) Symmetric Alignment for mutually reliable modality pairs, (ii) Directional Distillation where a reliable modality provides pseudo-supervision for its uncertain counterpart, and (iii) Reliability-Aware Slack Regularization that adaptively attenuates the influence of mutually unreliable samples without enforcing rigid geometric constraints. Extensive experiments on multiple benchmark datasets demonstrate that AdaPCL consistently outperforms baseline multimodal classification methods. Code is available at https://github.com/yuhongcqupt/AdaPCL.

A dilemma faced by classification is that the data is not collected at the same frequency in some applications. We investigate the mixed frequency data in a new way and recognize them as a special style of multi-view data, in which each view data is collected at a different sampling frequency. This paper proposes a discriminative dictionary pair learning method constrained by ordinal locality for mixed frequency data classification (shorted by DPLOL-MF). This method integrates synthesis dictionary and analysis dictionary into a dictionary pair, which not only improves computational cost caused by the ℓ0or ℓ1-norm constraint, but also can deal with the sampling frequency inconsistency. The DPLOL-MF utilizes a synthesis dictionary to learn class-specified reconstruction information and employs an analysis dictionary to generate coding coefficients by analyzing samples. Particularly, the ordinal locality preserving term is leveraged to constrain the atoms of dictionaries pair to further facilitate the learned dictionary pair to be more discriminative. Besides, we design a specific classification scheme for the inconsistent sample size of mixed frequency data. This paper illustrates a novel idea to solve the classification task of mixed frequency data and the experimental results demonstrate the effectiveness of the proposed method.

A dilemma faced by classification is that the data is not collected at the same frequency in some applications. We investigate the mixed frequency data in a new way and recognize them as a special style of multi-view data, in which each view data is collected at a different sampling frequency. This article proposes a discriminative dictionary pair learning method constrained by ordinal locality for mixed frequency data classification (shorted by DPLOL-MF). This method integrates synthesis dictionary and analysis dictionary into a dictionary pair, which not only improves computational cost caused by the${\ell _0}$or${\ell _1}$-norm constraint, but also can deal with the sampling frequency inconsistency. The DPLOL-MF utilizes a synthesis dictionary to learn class-specified reconstruction information and employs an analysis dictionary to generate coding coefficients by analyzing samples. Particularly, the ordinal locality preserving term is leveraged to constrain the atoms of dictionaries pair to further facilitate the learned dictionary pair to be more discriminative. Besides, we design a specific classification scheme for the inconsistent sample size of mixed frequency data. This paper illustrates a novel idea to solve the classification task of mixed frequency data and the experimental results demonstrate the effectiveness of the proposed method.

The existing multi-view clustering algorithms require that a sample in a view is completely or partially mapped onto one or more samples in a different corresponding view. However, this requirement could not be satisfied in many practical applications. Fortunately, there is a common cognition that the graph structure formed from each view should be as consistent as possible. Thus, this paper proposes a novel multi-view clustering method for unknown mapping relationships between cross-view samples based on the framework of non-negative matrix factorization, as an attempt to solve this problem. The objective function is designed by effectively building reconstruction error terms, local structural constraint terms, and cross-view mapping loss terms by exploring cross-view relationships. The experimental results show that the proposed method not only performs well to reveal the real mapping relationships between cross-view samples but also outperforms the comparison algorithms on the obtained clustering results.

Aluminum alloy is widely used in transportation, catering, industry, sports, health and other fields, because of their excellent high specific strength and corrosion resistance. Aluminum industry has been an important mainstay industry of a national economy. In the process of electrolytic aluminum, the superheat degree is a very important production target. When an electrolysis cell is working in the appropriate superheat degree state, the life of cell is prolonged and the amounts of aluminum released will enhanced. However, to measure the superheat degree is very difficult and the measured results cannot timely feedback to the process of production. To address the problem, a soft sensing prediction model of superheat degree is proposed in this paper, in which some new concepts such as the decay function of data weight, the credibility of a rule and the rule tree are introduced. Basically speaking, the processing of the soft sensing prediction model is mainly based on the rough set data analysis method and a tree data structure. The static rules are obtained from the history data by using the attribute reduction and value reduction method in the rough sets. The rule tree is updated timely based on the incremental data set accordingly. The effectiveness of the proposed model is verified with the aluminum production data provided by Shandong Weiqiao Aluminum Electrolysis limited company in China.

In the human cognition learning, the noise self-filtering filters the noise data by itself, making the human brain very robust. Although k-Nearest Neighbors (kNN) can decrease the affection of noise data by optimizing the parameter k, noise data still may deteriorate the learning results to an extent on various data. Therefore, this paper proposes the strategy for kNNs. Here, we formalize such training strategies in the context of kNNs, and they are called with “Noise Self-filtering k-Nearest Neighbors” (NSF-kNNs). As its name suggests, it is determined by the model itself based on what it has already learned, as oppose to some predefined heuristic criteria. We derive the mathematical model of the NSF-kNNs, which is an algorithm framework. The NSF-kNNs are compared with the exact kNNs and as far as we know the most efficient approximate kNN [1]. The results show the achievement of improvements in the robustness and generalizability on various data sets.