2021
DOI: 10.1109/jstars.2021.3074058
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Tensor-Based Learning Framework for Automatic Multichannel Volcano-Seismic Classification

Abstract: This work proposes a supervised tensor-based learning framework for classifying volcano-seismic events from signals recorded at the Ubinas volcano, in Peru, during a period of great activity in 2009. The proposed method is fully tensorial, as it integrates the three main steps of the automatic classification system (feature extraction, dimensionality reduction and classifier) in a general multidimensional framework for tensor data, joining tensor learning techniques such as the Multilinear Principal Component … Show more

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Cited by 6 publications
(1 citation statement)
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“…To address the above issues, Tao et al [ 29] proposed a supervised tensor learning framework and the support vector machine was extended to support tensor machine (STM), making full use of the structural information and correlation of data. At present, STM has received attention and application in other fields (e.g., remote sensing image recognition [ 30 ], pedestrian detection in thermal infrared images [ 31 ] and volcanic earthquake classification [ 32 ]) Motivated by the application of kernels in SVM, Li et al [33]] developed a kernelled support tensor machine (KSTM) iterative algorithm with a convergent proof to train the weight vectors for every mode of the tensor for the classifier. To increase the expressive power of the model.…”
mentioning
confidence: 99%
“…To address the above issues, Tao et al [ 29] proposed a supervised tensor learning framework and the support vector machine was extended to support tensor machine (STM), making full use of the structural information and correlation of data. At present, STM has received attention and application in other fields (e.g., remote sensing image recognition [ 30 ], pedestrian detection in thermal infrared images [ 31 ] and volcanic earthquake classification [ 32 ]) Motivated by the application of kernels in SVM, Li et al [33]] developed a kernelled support tensor machine (KSTM) iterative algorithm with a convergent proof to train the weight vectors for every mode of the tensor for the classifier. To increase the expressive power of the model.…”
mentioning
confidence: 99%