Abstract: The initial proximity matrix affects the performance of most proximity-based multi-view clustering methods. Initial value sensitivity problem. Tuning the initial proximity matrix is unrealistic for unsupervised clustering. Thus, proximity-based multi-view clustering’s initial value sensitivity…
Abstract: Million-point large-scale subspace clustering (LS 2 C) is examined in this article. Despite being state-of-the-art for small-scale data points, many subspace clustering methods cannot directly solve the LS 2 C problem. These methods use…
Abstract: Many real-world applications require learning over massive, distributed data. With the rise of smart mobile devices and Internet of Things (IoT) devices, privacy and security concerns make data sharing difficult. Federated learning can jointly…
Abstract: Audio signal processing values music information retrieval. Musical instrument playing techniques have been neglected. This study proposes a GPT classification system. Because playing techniques vary slightly, GPT classification is difficult. This research uses spectral–temporal…
Abstract: Recommender systems provide relevant products, information, and services to users. Deep reinforcement learning has been successfully applied to recommender systems, but data sparsity and cold-start still plague real-world tasks. We propose using users’ pervasive…
Abstract: Since many real-world applications require semantic understanding of short texts, inferring discriminative and coherent latent topics is crucial. Since short texts have little word co-occurrence information, traditional long text topic modeling algorithms like PLSA…
Abstract: Because ensemble learning improves classification model prediction, it has many applications. This article proposes semisupervised multiple choice learning (SemiMCL) to jointly train a network ensemble on partially labeled data. Our model improves labeled data…
Abstract: Deep neural networks can now generate real-world data. To overcome the posterior distribution’s intractability, implicit probabilistic models that generate data stochastically have been introduced. However, data modeling requires deep statistical dependence knowledge, which can…
Abstract: Many node embedding and network representation methods use random walks. However, using traditional random walks results in embeddings that capture proximity (communities) rather than structural similarity (roles). Due to node identity, embeddings cannot be…
Abstract: The lifelong machine learning paradigm learns a sequence of tasks based on previous experiences, such as knowledge library or deep network weights. Most recent lifelong learning models have knowledge libraries or deep networks of…
Abstract: Event-based Social Network (EBSN) connects online and offline social relationships. EBSNs publish more events, making personalized event recommendation essential to help users choose appealing events. Most event recommendation algorithms fail to distinguish constraint factors…
Abstract: Momentum technique has improved deep learning and regularized learning by accelerating gradient descent (GD) convergence. NAG and HB are momentum methods. Most acceleration analyses have focused on NAG, with a few on HB. This…
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