Extracting and Composing Robust Features With Broad Learning System

Yang, Kaixiang; Liu, Yuchen; Yu, Zhiwen; Chen, C. L. Philip

doi:10.1109/tkde.2021.3137792

Cited by 26 publications

(7 citation statements)

References 44 publications

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“…Evaluation Metrics. The accuracy of a test model for missing data predictions can be measured by the root mean square error (RMSE) [15][16][17][18]30]: I. In all experiments on each dataset, we randomly split its known set of entries Λ into ten disjoint subsets for tenfold cross-validation.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Generalized Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks

Zhong

Luo

2021

2021 International Joint Conference on Neural Networks (IJCNN)

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An Undirected Weighted Network (UWN) is frequently encountered in a big-data-related application concerning the complex interactions among numerous nodes, e.g., a protein interaction network from a bioinformatics application. A Symmetric High-Dimensional and Incomplete (SHDI) matrix can smoothly illustrate such an UWN, which contains rich knowledge like node interaction behaviors and local complexes. To extract desired knowledge from an SHDI matrix, an analysis model should carefully consider its symmetric-topology for describing an UWN's intrinsic symmetry. Representation learning to an UWN borrows the success of a pyramid of symmetry-aware models like a Symmetric Nonnegative Matrix Factorization (SNMF) model whose objective function utilizes a sole Latent Factor (LF) matrix for representing SHDI's symmetry rigorously. However, they suffer from the following drawbacks: 1) their computational complexity is high; and 2) their modeling strategy narrows their representation features, making them suffer from low learning ability. Aiming at addressing above critical issues, this paper proposes a Multi-constrained Symmetric Nonnegative Latent-factor-analysis (MSNL) model with two-fold ideas: 1) introducing multi-constraints composed of multiple LF matrices, i.e., inequality and equality ones into a data-density-oriented objective function for precisely representing the intrinsic symmetry of an SHDI matrix with broadened feature space; and 2) implementing an Alternating Direction Method of Multipliers (ADMM)-incorporated learning scheme for precisely solving such a multi-constrained model. Empirical studies on three SHDI matrices from a real bioinformatics or industrial application demonstrate that the proposed MSNL model achieves stronger representation learning ability to an SHDI matrix than state-of-the-art models do.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Generalized Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks

Zhong

Luo

2021

2021 International Joint Conference on Neural Networks (IJCNN)

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“…where X is the input data and W f and β f are the weighting matrix and bias of feature nodes, respectively. Via the sparse autoencoder (Yang et al, 2021), BLS uses the mapping function ϕ to extend the enhancement nodes. The m th group of the nodes can be expressed as follows:…”

Section: Gaussianmentioning

confidence: 99%

Spatio-temporal prediction of photovoltaic power based on a broad learning system and an improved backtracking search optimization algorithm

Tang,

Huang,

Qian

et al. 2024

Front. Energy Res.

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The accuracy of photovoltaic (PV) power forecasting techniques relies not only on high-quality spatiotemporal data but also on an efficient feature-mining methodology. In this study, a spatiotemporal power forecasting model based on the broad learning system (BLS) and the improved backtracking search optimization algorithm (IBSOA) is proposed. The objective is to enhance the accuracy of PV power predictions while reducing the time-intensive training process associated with an extensive set of broad learning system parameters. The spatiotemporal attributes of historical data from multiple PV sites are clustered using a self-organizing map. The clustering analysis explores the spatiotemporal correlation among five photovoltaic (PV) power stations for each season between 2017 and 2018. Subsequently, the IBSOA is employed to optimize the hyperparameters of the BLS model, particularly the mapping and enhancement nodes. By utilizing hyperparameter optimization, a BSOA-based broad learning model is introduced to achieve superior accuracy. The results are assessed using the proposed method in comparison with three popular optimization algorithms: 1) genetic algorithm (GA), 2) bird swarm algorithm (BSA), and 3) backtracking search optimization algorithm (BSOA). All scenarios are validated and compared using PV plant data from the DKA center in Australia. The root-mean-square error (RMSE) indicators of the proposed prediction method are consistently lower than the worst-case scenario in each season, decreasing by 3.2283 kW in spring, 3.9159 kW in summer, 1.3425 kW in autumn, and 1.4058 kW in winter. Similarly, the mean absolute percentage error (MAPE) exhibits a reduction compared to the worst case, with a decreases of 0.882% in spring, 1.2399% in summer, 1.803% in autumn, and 1.087% in winter. The comprehensive results affirm that the proposed method surpasses alternative optimization techniques, delivering high-quality power forecasts for the given case study.

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“…In the intrusion detection methods of the DAS system, the pattern recognition algorithms can classify vibration events (Xu et al 2017;Wu et al 2020b and achieved an effective recognition rate of more than 95% in the real environment . More effective feature extraction was the key to improving the recognition accuracy (Liu et al 2022;Yang et al 2021a). New research began to try feature extraction and recognition technology based on multidimensional signals (Liang et al 2019;Li et al 2023).…”

Section: Related Workmentioning

confidence: 99%

AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System

Zhu,

Yang,

Yang

et al. 2024

AAAI

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The application of artificial intelligence technology has greatly enhanced and fortified the safety of energy pipelines, particularly in safeguarding against external threats. The predominant methods involve the integration of intelligent sensors to detect external vibration, enabling the identification of event types and locations, thereby replacing manual detection methods. However, practical implementation has exposed a limitation in current methods - their constrained ability to accurately discern the spatial dimensions of external signals, which complicates the authentication of threat events. Our research endeavors to overcome the above issues by harnessing deep learning techniques to achieve a more fine-grained recognition and localization process. This refinement is crucial in effectively identifying genuine threats to pipelines, thus enhancing the safety of energy transportation. This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. Specifically, we introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features and construct a threat estimation and recognition network. The utilization of collected acoustic signal data is optimized, and the underlying principle is elucidated. Moreover, we incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency. Empirical evidence gathered from real-world scenarios underscores the efficacy of our method, notably in its substantial reduction of false alarms and remarkable gains in recognition accuracy. More generally, our method exhibits versatility and can be extrapolated to a broader spectrum of recognition tasks and scenarios.

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Extracting and Composing Robust Features With Broad Learning System

Cited by 26 publications

References 44 publications

Generalized Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks

Generalized Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks

Spatio-temporal prediction of photovoltaic power based on a broad learning system and an improved backtracking search optimization algorithm

AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System

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