Abstract: Semi-supervised learning is one of the most appealing machine learning problems. This paper introduces Semi-supervised Adaptive Local Embedding learning (SALE), a locality-preserved dimensionality reduction framework that learns a local discriminative embedding by constructing a…
Abstract: Text mining, visual classification, and recommender systems use machine learning to make accurate predictions from data. When processing large datasets, most sophisticated machine learning methods take a long time. Large-scale machine learning (LML) is…
Abstract: Data similarity (or distance) computation is a fundamental research topic that underpins many high-level machine learning and data mining applications based on similarity measures. Big data’s “3V” nature—volume, velocity, and variety—makes exact similarity computation…
Abstract: Hybrid systems, which combine statistical and machine learning (ML) techniques using residual (error forecasting) modeling, have been highlighted in the literature for their accuracy and ability to forecast time series with different characteristics. Due…
Abstract: The explosion of modeling complex systems using attributed networks boosts anomaly detection research in such networks, which can be applied in many high-impact domains. Many existing attempts simply concatenate multiple views into a feature…
Abstract: Many NoSQL and SQL systems use log-structured merge (LSM) tree key-value (KV) stores for online big data applications like social networking, graph processing, machine learning, etc. In LSM-tree key-value stores, batch processing of sorted…
Abstract: Many reviews-rating integration studies have improved recommendation performance. These works still have flaws: (1) Dynamically integrating review and interaction data features is often overlooked, but treating them equally may lead to a faulty understanding…
Abstract: Traffic congestion has become a major economic and environmental problem for urban areas worldwide. Traffic prediction is an effective way to reduce congestion. Traffic prediction research has grown since the late 1970s. Early studies…
Abstract: Recommender systems model user preferences to reduce information explosion and improve online application user experience. Recommender systems still face data sparsity and cold-start issues despite many efforts. Recommending with knowledge graph side information has…
Abstract: Deep neural networks excel at many benchmark vision tasks thanks to large labeled training datasets. Obtaining large amounts of labeled data is costly and time-consuming in many applications. Many have tried to directly apply…
Abstract: Self-Sovereign Identity (SSI) is a novel and emerging decentralized digital identity approach that allows entities to control and manage their digital identifiers and identity data while improving trust, privacy, security, and many other properties…
Abstract: In the COVID-19 pandemic, activity-tracking and location-based services using short-range communication (SRC) have become increasingly popular, especially for automated contact tracing. 1) How to safeguard data and location privacy? 2) how to efficiently and…
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