Although multi-view unsupervised feature selection is a promising technique for dimensionality reduction on unlabeled multi-view data, existing methods cannot directly address incomplete data, where certain samples are missing in specific views. These methods typically begin by imputing missing data using predetermined values, followed by performing feature selection on the completed dataset. However, the separation of imputation and feature selection processes fails to exploit their inherent synergy, as local structural information obtained from feature selection could guide the imputation process and, in turn, improve the overall effectiveness of feature selection. In addition, previous methods rely on similarity graphs based on Euclidean distance to preserve the local manifold structure but overlook the topological relationships within the data, thereby hindering accurate capture of intrinsic structures. In this paper, we propose an adaptive topological similarity learning for incomplete multi-view unsupervised feature selection method (ATSL-IMUFS) to address the aforementioned issues. ATSL-IMUFS first integrates multi-view feature selection and missing data imputation into a unified learning framework. Then, it adaptively learns similarity graphs for each view while simultaneously capturing the consensus topological relationship across views, effectively characterizing the local manifold structure. Extensive experiments conducted on real-world datasets demonstrate the superior performance of ATSL-IMUFS compared to competing methods.

Unsupervised feature selection (UFS) has recently gained attention for its effectiveness in processing unlabeled high-dimensional data. However, existing methods overlook the intrinsic causal mechanisms within the data, resulting in the selection of irrelevant features and poor interpretability. Additionally, previous graph-based methods fail to account for the differing impacts of non-causal and causal features in constructing the similarity graph, which leads to false links in the generated graph. To address these issues, a novel UFS method, called Causally-Aware UnSupErvised Feature Selection learning (CAUSE-FS), is proposed. CAUSE-FS introduces a novel causal regularizer that reweights samples to balance the confounding distribution of each treatment feature. This regularizer is subsequently integrated into a generalized unsupervised spectral regression model to mitigate spurious associations between features and clustering labels, thus achieving causal feature selection. Furthermore, CAUSE-FS employs causality-guided hierarchical clustering to partition features with varying causal contributions into multiple granularities. By integrating similarity graphs learned adaptively at different granularities, CAUSE-FS increases the importance of causal features when constructing the fused similarity graph to capture the reliable local structure of data. Extensive experimental results demonstrate the superiority of CAUSE-FS over state-of-the-art methods, with its interpretability further validated through feature visualization.

Tabular data is one of the most widely used formats across industries, driving critical applications in areas such as finance, healthcare, and marketing. In the era of data-centric AI, improving data quality and representation has become essential for enhancing model performance, particularly in applications centered around tabular data. This survey examines the key aspects of tabular data-centric AI, emphasizing feature selection and feature generation as essential techniques for data space refinement. We provide a systematic review of feature selection methods, which identify and retain the most relevant data attributes, and feature generation approaches, which create new features to simplify the capture of complex data patterns. This survey offers a comprehensive overview of current methodologies through an analysis of recent advancements, practical applications, and the strengths and limitations of these techniques. Finally, we outline open challenges and suggest future perspectives to inspire continued innovation in this field.

Multi-view unsupervised feature selection (MUFS) has received considerable attention in recent years. Existing MUFS methods for processing unlabeled incomplete multi-view data, where some samples are missing in certain views, first impute the missing values and then perform feature selection on the completed dataset. However, treating imputation and feature selection as two separate processes overlooks their potential interactions. The graph-guided local structure gleaned from feature selection can aid in imputation, which in turn can enhance the feature selection performance. Additionally, most similarity graph-based MUFS methods suffer from high computational costs. To address these problems, we propose a novel MUFS method, termed Tensorial Incomplete Multi-view unsupErvised Feature Selection (TIME-FS). TIME-FS unifies missing value recovery, discriminative feature selection, and low-dimensional representation learning within a joint framework through matrix decomposition. Then, TIME-FS conducts CP decomposition on tensor data formed by the low-dimensional representations of different views to learn a consistent anchor graph across views and a view-preference weight matrix, both of which simultaneously guide missing view imputation and feature selection. Furthermore, an efficient algorithm with low time complexity and rapid convergence is proposed to solve TIME-FS. Extensive experimental results demonstrate the effectiveness and efficiency of TIME-FS over state-of-the-art methods.

Iterative feature space optimization includes continuously evaluating and refining the feature space to improve downstream task performance. However, existing methods commonly suffer from three major limitations: 1) ignoring differences between samples leads to evaluation bias; 2) the feature space is overly tailored to specific models, resulting in overfitting and poor generalization; and 3) retraining the evaluator from scratch in each iteration significantly reduces overall efficiency. To bridge these gaps, we introduce EASE (gEneralized Adaptive feature Space Evaluator), a generalized framework for efficient and objective evaluation of iteratively generated feature spaces. This framework includes two key components: Feature-Sample Subspace Generator and Contextual Attention Evaluator. The first component aims to mitigate evaluation bias by decoupling the information distribution within the feature space. To achieve this, based on feedback from the subsequent evaluator, we identify the samples most challenging for evaluation and the features most relevant to prediction tasks. The second component intends to incrementally capture evolving patterns of the feature space for efficient evaluation. Specifically, we propose a weighted-sharing multi-head attention mechanism to encode the feature space into an embedding vector for evaluation, and update the evaluator incrementally to retain prior knowledge while incorporating new information. Extensive experiments on fifteen public datasets demonstrate the effectiveness of EASE. We have released our code and data to the public.

Multimodal recognition can achieve enhanced performance by leveraging the complementary information from different modali- ties. However, in real-world scenarios, multimodal samples often express discordant semantic meanings across modalities, lacking evident complementary information. Unlike humans who can easily understand the intrinsic semantic information of these semantically discordant samples, existing multimodal recognition models show poor performance on them. With the motivation of improving the robustness of multimodal recognition models in practical scenar- ios, this work poses a new challenge in multimodal recognition, which is coined as Semantic Discordance Understanding. Unlike ex- isting works only focusing on detecting semantically discordant samples as noisy data, this new challenge requires deep models to follow humans’ ability in understanding the inherent seman- tic meanings of semantically discordant samples. To address this challenge, we further propose the Progressive Multimodal Pivot Learning (PMPL) approach by introducing a learnable pivot mem- ory to explore the inherent semantics meaning hidden under dis- cordant modalities. To this end, our approach inserts Pivot Memory Learning (PML) modules into multiple layers of unimodal foun- dation models to progressively trade-off the conflict information across modalities. By introducing the multimodal pivot learning paradigm for multimodal recognition, the proposed PMPL approach can alleviate the negative effect of semantic discordance caused by the cross-modal information exchange mechanism of existingmultimodal recognition models. Experiments on different bench- marks validate the superiority of our approach. Code is available at https://github.com/tiggers23/PMPL.

Indoor temperature prediction is crucial for decision-making in central heating systems. Beyond accuracy, predictions shall be interpretable, i.e. conform to the laws of physics; otherwise, it may lead to system failures or unsafe conditions. However, deep learning models often face criticism regarding interpretability, which limits their application in such settings. To this end, we propose a Spatio-Temporal Consistency enhanced Differential Network (CONST) for interpretable indoor temperature prediction. Our approach mainly consists of a differential predictive module and a spatio-temporal consistency module. Modeling the influential factors, the first module solves the issue of multicollinearity through the differential operation. Considering the heterogeneity of global and local data distributions, the second module characterizes the temporal and spatial consistency to mine the universal pattern by multi-task learning, thereby improving the prediction interpretability. Besides, we propose a set of interpretability metrics to overcome the drawbacks of partial dependence plot metric, which are more practical, zero-centered, flexible, and numerical. We conclude experiments on a real-world dataset with four heating stations. The results demonstrate the advantages of our approach over various baselines, where the interpretability can be improved by more than 8 times on cRPD while maintaining high accuracy. We developed CONST on the SmartHeat system, providing hourly indoor temperature forecasts for 13 heating stations in northern China.

Multi-view unsupervised feature selection (MUFS) has been demonstrated as an effective technique to reduce the dimensionality of multi-view unlabeled data. The existing methods assume that all of views are complete. However, multi-view data are usually incomplete, i.e., a part of instances are presented on some views but not all views. Besides, learning the complete similarity graph, as an important promising technology in existing MUFS methods, cannot achieve due to the missing views. In this paper, we propose a complementary and consensus learning-based incomplete multi-view unsupervised feature selection method (C$^{2}$IMUFS) to address the aforementioned issues. Concretely, C$^{2}$IMUFS integrates feature selection into an extended weighted non-negative matrix factorization model equipped with adaptive learning of view-weights and a sparse$\ell _{2,p}$-norm, which can offer better adaptability and flexibility. By the sparse linear combinations of multiple similarity matrices derived from different views, a complementary learning-guided similarity matrix reconstruction model is presented to obtain the complete similarity graph in each view. Furthermore, C$^{2}$IMUFS learns a consensus clustering indicator matrix across different views and embeds it into a spectral graph term to preserve the local geometric structure. Comprehensive experimental results on real-world datasets demonstrate the effectiveness of C$^{2}$IMUFS compared with state-of-the-art methods.

The natural gas tightly correlates with our everyday life. However, driven by gray incomes, some users are prone to stealing gas by refitting the equipment without permission. Especially for the boiler room users in winter, this phenomenon appears more rampant. Traditional gas-theft detection methods highly rely on the on-site inspection, where exists ineffective and randomness. With the rapidly deployed IoT sensors, we can collect real-time gas consumption data to analyze users’ behavior patterns, where the gas-theft suspects could be discovered early and accurately. In this paper, we propose a data-driven approach, named SVOC, to detect gas-theft suspects among boiler room users. Our approach consists of a scenario-based data quality detection algorithm, a deformation-based normality detection algorithm, and an One-Class Support Vector Machine (OCSVM) based anomaly detection algorithm. Specifically, considering the temporal proximity between the gas consumption and the outdoor temperature, the normality detection algorithm adopts a similarity-based deformation correlation to detect normal boiler room users out of abnormal ones. Then, we employ OCSVM as the anomaly detection algorithm to capture various features across multiple data sources, aiming to distinguish gas-theft suspects from the remaining irregular users. Here, the detected normal and abnormal users are fed into the OCSVM for training and prediction, respectively, which can overcome the label scarcity problem. We conduct extensive experiments on a real-world dataset during one heating season. The results demonstrate distinct advantages of our approach over various baselines. We have developed a real-time system on the cloud, providing daily gas-theft suspects for gas companies.

Human multimodal emotion recognition involves time-series data of different modalities, such as natural language, visual motions, and acoustic behaviors. Due to the variable sampling rates for sequences from different modalities, the collected multimodal streams are usually unaligned. The asynchrony across modalities increases the difficulty on conducting efficient multimodal fusion. Hence, this work mainly focuses on multimodal fusion from unaligned multimodal sequences. To this end, we propose the Progressive Modality Reinforcement (PMR) approach based on the recent advances of crossmodal transformer. Our approach introduces a message hub to exchange information with each modality. The message hub sends common messages to each modality and reinforces their features via crossmodal attention. In turn, it also collects the reinforced features from each modality and uses them to generate a reinforced common message. By repeating the cycle process, the common message and the modalities’ features can progressively complement each other. Finally, the reinforced features are used to make predictions for human emotion. Comprehensive experiments on different human multimodal emotion recognition benchmarks clearly demonstrate the superiority of our approach.

Information fusion is capable of fusing and transforming multiple data derived from different sources to provide a unified representation for centralized knowledge mining that facilitates effective decision-making, classification and prediction, etc. Multisource interval-valued data, characterizing the uncertainty phenomena in the data in the form of intervals in different sources, are the most common symbolic data which widely exist in many real-world applications. This paper concentrates on efficient fusing of multisource interval-valued data with the dynamic updating of data sources, involving the addition of new sources and deletion of obsolete sources. We propose a novel data fusion method based on fuzzy information granulation, which translates multisource interval-valued data into trapezoidal fuzzy granules. Given this effectively fusing capability, we develop incremental mechanisms and algorithms for fusing multisource interval-valued data with a dynamic variation of data sources. Finally, extensive experiments are carried out to verify the effectiveness of the proposed algorithms when comparing to six different fusion algorithms. Experimental results show that the proposed fusion method outperforms other related approaches. Furthermore, the proposed incremental fusion algorithms can reduce the computing overhead in comparison with the static fusion algorithm when adding and deleting multiple data sources.