Online learning with feature spaces that are not fixed but can vary over time renders a seemingly flexible learning paradigm thus has drawn much attention. Unfortunately, two restrictions prohibit a ubiquitous application of this learning paradigm in practice. First, whereas prior studies mainly assume a homogenous feature type, data streams generated from real applications can be heterogeneous in which Boolean, ordinal, and continuous co-exist. Existing methods that prescribe parametric distributions such as Gaussians would not suffice to model the correlation among such mixtyped features. Second, while full supervision seems to be a default setup, providing labels to all arriving data instances over a long time span is tangibly onerous, laborious, and economically unsustainable. Alas, a semi-supervised online learner that can deal with mix-typed, varying feature spaces is still missing. To fill the gap, this paper explores a novel problem, named Online Semi-supervised Learning with Mixtyped streaming Features (OSLMF), which strives to relax the restrictions on the feature type and supervision information. Our key idea to solve the new problem is to leverage copula model to align the data instances with different feature spaces so as to make their distance measurable. A geometric structure underlying data instances is then established in an online fashion based on their distances, through which the limited labeling information is propagated, from the scarce labeled instances to their close neighbors. Experimental results are documented to evidence the viability and effectiveness of our proposed approach. Code is released in https://github.com/wudi1989/OSLMF.

Feature selection, an essential technique in data mining, is often confined to batch learning or online idealization of data scenarios despite its significance. Existing online feature selection methods have specific assumptions regarding the data stream, such as requiring a fixed feature space with an explicit pattern and complete labeling of samples. Unfortunately, data streams generated in many real scenarios commonly exhibit arbitrarily incomplete feature spaces and scarcity labels, making existing approaches unsuitable for real applications. To fill these gaps, this study proposes a new problem called Online Feature Selection with Varying Features Spaces (OFSVF). OFSVF has a three-fold main idea: 1) it leverages Gaussian Copula to model the incomplete feature correlation in a complete latent space, encoded by continuous variables, 2) it employs a novel tree-ensemble-based approach to select the most informative features on-the-fly, and 3) it develops the underlying geometric structure of instances to establish the relationship between unlabeled and labels. Experimental results are documented to demonstrate the feasibility and effectiveness of our proposed method.

Recommender systems have long struggled with challenges such as cold start and data sparsity, which can lead to poor recommendation performance. While previous approaches have attempted to address these issues by incorporating side information, they often introduce noise, lack flexibility for data expansion, and suffer from inconsistent data quality—factors that hinder accurate user preference inference and reduce recommendation performance. With the vast knowledge bases and advanced reasoning capabilities of large language models (LLMs), these models are particularly well-suited to supplement auxiliary information and capture implicit user intent. To address these challenges, we propose a novel framework, ER2ALM, which leverages the capabilities of LLMs enhanced by Retrieval-Augmented Generation (RAG) to improve recommendation outcomes. Our framework specifically addresses the challenges by flexibly and accurately augmenting auxiliary information and capturing users’ implicit preferences and interests. Additionally, to mitigate the risk of introducing noise, we incorporate a noise reduction strategy to ensure the reliability of the augmented information. Experimental validation on two real-world datasets demonstrates the efficacy of our approach, significantly enhancing both the accuracy and robustness of recommendations compared to state-of-the-art methods. This demonstrates the potential of our framework as a new paradigm for preference mining in recommendation systems.

The Internet of Things (IoT) collects large volumes of diverse data, with graph data as a critical component, and extensively utilizes Federated Graph Learning (FGL) to process this data while preserving data security. However, the graph data collected by different IoT institutions are relatively independent due to various factors (e.g., data collection methods, geographical locations), and data access is restricted to local environments due to privacy constraints, IoT institutions typically possess heterogeneous feature spaces. Aggregating under these conditions could potentially contaminate local graph representations. Unfortunately, most existing FGL methods tend to overlook this issue. To address this challenge, we propose FGL via Constructing and Sharing Features (FedCSF), a novel FGL framework designed to build a globally consistent feature space and share it among IoT institutions. To construct and extract the feature space, we employ an uniform feature initialization across IoT institutions and design an encoder to extract both global and local feature relationships, thereby facilitating effective collaboration across data from different IoT institutions. Furthermore, we introduce an independent adaptive aggregation strategy to eliminate the integration of harmful knowledge, ensuring that the contributions of each IoT institution are effectively integrated into the local model. We theoretically analyze the convergence of FedCSF. To validate the effectiveness of FedCSF, we conducted extensive experiments under various settings (i.e., cross-datasets, and cross-domains), demonstrating its significant advantages of FedCSF in terms of performance, convergence speed, and practical adaptability.

Online learning, where feature spaces can change over time, offers a flexible learning paradigm that has attracted considerable attention. However, it still faces three significant challenges. First, the heterogeneity of real-world data streams with mixed feature types presents challenges for traditional parametric modeling. Second, data stream distributions can shift over time, causing an abrupt and substantial decline in model performance. Additionally, the time and cost constraints make it infeasible to label every data instance in a supervised setting. To overcome these challenges, we propose a new algorithm Online Learning from Mix-typed, Drifted, and Incomplete Streaming Features (OL-MDISF), which aims to relax restrictions on both feature types, data distribution, and supervision information. Our approach involves utilizing copula models to create a comprehensive latent space, employing an adaptive sliding window for detecting drift points to ensure model stability, and establishing label proximity information based on geometric structural relationships. To demonstrate the model’s efficiency and effectiveness, we provide theoretical analysis and comprehensive experimental results.

Online learning, characterized by its feature space’s adaptability over time, has emerged as a flexible learning paradigm that has attracted widespread attention. However, existing online learning methods often overlook the distributional differences between instances and the presence of label noise in streaming data, thus significantly hindering the effectiveness and robustness of these algorithms. To overcome these challenges, we propose an online confidence learning algorithm for noisy labeled features, which aims to achieve robustness against arbitrary data streams and noisy labels. It employs two new strategies: online confidence inference, which applies the principle of empirical risk minimization to identify inconsistencies in spatial distributions, and geometric structure learning, which utilizes dynamic instance confidence to compute disparities between instances and their labels. Empirical findings demonstrate that our label correction mechanism enhances classification accuracy more effectively across various types of noisy labels (i.e., symmetric, asymmetric, and flipped). Additionally, a case study on image datasets was conducted to illustrate in detail the effectiveness of our OLNLS algorithm. Code is released in https://github.com/Zhuosd/OLNLS .