Distributed semi-supervised partial multi-dimensional learning via subspace learning
Abstract Multi-dimensional classification (MDC) aims to simultaneously train a number of multi-class classifiers for multiple heterogeneous class spaces. However, as supervised learning methods, the existing MDC algorithms require that all the training data be precisely labeled in multi-dimensional...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-06-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01942-5 |
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| Summary: | Abstract Multi-dimensional classification (MDC) aims to simultaneously train a number of multi-class classifiers for multiple heterogeneous class spaces. However, as supervised learning methods, the existing MDC algorithms require that all the training data be precisely labeled in multi-dimensional class spaces, which can be impractical in many real applications sometimes. The lack of high-quality labeled data may negatively affect their learning performance. Additionally, the existing MDC algorithms only address scenarios of centralized processing, where all training data must be centrally stored at a single fusion center. Nowadays, however, the training data are typically distributed at multiple nodes within a network, making it challenging to transmit them to a fusion center for further processing. To address these issues, in this paper, we propose a novel algorithm called distributed semi-supervised partial multi-dimensional learning (dS $$^2$$ 2 PMDL), which is designed to handle distributed classification of a small proportion of partially multi-dimensional (PMD) data and a large proportion of unlabeled data across a network. In our proposed algorithm, an in-network framework of subspace learning is formulated for label recovery. By tracking the representations of non-noisy label vectors in the learned subspace, the reliable labels of training data can be recovered. Subsequently, the multi-dimensional classifier modeled by the random feature map can be adaptively trained using a two-level label dependencies exploitation strategy. The convergence performance and communication complexity of the dS $$^2$$ 2 PMDL algorithm are analyzed. Furthermore, experiments on multiple datasets are performed to validate the effectiveness of the proposed algorithm in semi-supervised partial multi-dimensional classification. |
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| ISSN: | 2199-4536 2198-6053 |