Semi-supervised one-pass multi-view learning

Zhu, Changming; Wang, Zhe; Zhou, Ri-Gui; Wei, Lai; Zhang, Xiafen; Ding, Yi

doi:10.1007/s00521-018-3654-3

Cited by 6 publications

(15 citation statements)

References 30 publications

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“…En [29] las vistas son estructuradas considerando las partes de la url de un documento: base, texto de la imagen de la url y el destino de la url, así se arma conjuntos de datos en las tres dimensiones. O el estudio de [30] que recopila conjuntos de documentos multimedia y prepara vistas de entrenamiento diferentes con el video, audio y texto del documento.…”

Section: Co-entrenamientounclassified

“…Entre las fortalezas de estos modelos se identifica estructuras que no tienen límite de generación de vistas [31] o que aprovechan el conjunto de no etiquetados para entrenarlos en una vista independiente [30]. Además, se determina diseños para el análisis de documentos de alta escala (video, audio) [28], así como también documentos con actualización en tiempo real (big data) [30].…”

Section: Co-entrenamientounclassified

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Semi-supervised learning models for document classification: A systematic review and meta-analysis

Cevallos-Culqui,

Pons,

Rodriguez

2023

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The continuous increase of digital documents on the web creates the need to search for information patterns that allow the categorization of organizational documents to generate knowledge in an institution. An Artificial Intelligence technique for this purpose is text classification, it for its application uses labels (previously categorized documents) with supervised (with labels) or unsupervised (without labels) training models. Both traditional models with their advantages and disadvantages have been joined into semi-supervised models that extract the best qualities of each one, however, the labeling process involves resources and time that try to be optimized to improve classification accuracy. An analysis of the different semi-supervised models would show us the advantages of their training and the way how the structure of each of them affects the accuracy of their classification. In the present study, a classification structure of the semi-supervised models in the classification of documents is proposed to analyze their qualities and categorization process, through an SLR (Revision of systematic literature) that extracts performance metrics from the identified studies to perform a meta-analysis through forest plots. To define the search strategy for studies, the PICOC (Population, Intervention, Comparison, Outputs, Context) method has been used, it is supported by the research question defines a search string, which has allowed the collection of 228 research, these are filtered with the PRISMA declaration method and the determination of exclusion criteria, in this way 35 researches are selected for the present study. The analysis of the selected studies identifies a structure for the different semi-supervised learning models, and a scheme of their work process is obtained, it has been used to extract advantages, disadvantages, and performance metrics. Through a meta-analysis with forest diagrams, the classification accuracy performance of the researches in each learning model is evaluated, determining as results that regardless of the characteristics of its process, active learning (0.89) and assembled learning (0.83) present the best performance levels.

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Section: Co-entrenamientounclassified

Semi-supervised learning models for document classification: A systematic review and meta-analysis

Cevallos-Culqui,

Pons,

Rodriguez

2023

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“…Unfortunately, there are few online multi-view learning methods for multi-view data. OPMV (Zhu et al 2015) is one of the few online multi-view classification models. This approach jointly optimizes the composite objective functions with consistency linear constraints for different views.…”

Section: Related Workmentioning

confidence: 99%

“…-OPMV (Zhu et al 2015): it is an online multi-view learning. According to the paper, the learning rate parameter are chose from 2 [−8:8] , the regularization parameter are chose from 1e [−16:0] , and the penalty parameters is pre-defined as 1.…”

Section: Competitorsmentioning

confidence: 99%

Online Bayesian max-margin subspace learning for multi-view classification and regression

Zhuang

et al. 2019

Mach Learn

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Multi-view data have become increasingly popular in many real-world applications where data are generated from different information channels or different views such as image + text, audio + video, and webpage + link data. Last decades have witnessed a number of studies devoted to multi-view learning algorithms, especially the predictive latent subspace learning approaches which aim at obtaining a subspace shared by multiple views and then learning models in the shared subspace. However, few efforts have been made to handle online multiview learning scenarios. In this paper, we propose an online Bayesian multi-view learning algorithm which learns predictive subspace with the max-margin principle. Specifically, we first define the latent margin loss for classification or regression in the subspace, and then cast the learning problem into a variational Bayesian framework by exploiting the pseudolikelihood and data augmentation idea. With the variational approximate posterior inferred from the past samples, we can naturally combine historical knowledge with new arrival data, in a Bayesian passive-aggressive style. Finally, we extensively evaluate our model on several real-world data sets and the experimental results show that our models can achieve superior performance, compared with a number of state-of-the-art competitors. Keywords Multi-view learning • Online learning • Bayesian subspace learning • Max-margin • Classification • Regression Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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“…To manipulate instance evolving problem, online learning, which can be traced back to Perceptron algorithm [Ros58], is a standard learning paradigm and there are plenty of researches recently [CP00, CBL06, HAK07, ZYJZ15]. One-pass learning, as a special case of online learning, has also attracted many research interests in recent years [GJZZ13,ZGZ15]. To solve feature incremental and decremental problem, there are some researches concerning missing and corrupted features, such as [GR06, DS08, TGRS07, HLM15].…”

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confidence: 99%

One-Pass Learning with Incremental and Decremental Features

Hou

Zhou

2018

IEEE Trans. Pattern Anal. Mach. Intell.

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In many real tasks the features are evolving, with some features vanished and some other features being augmented. For example, in environment monitoring some sensors expired whereas some new ones were deployed; in mobile game recommendation some games dropped whereas some new ones were added. Learning with such incremental and decremental features is crucial but rarely studied, particularly when the data comes like a stream and thus it is infeasible to keep the whole data for optimization. In this paper, we study this challenging problem and present the OPID approach. Our approach attempts to compress important information of vanished features into functions of survived features, and then expand to include the augmented features. It is an one-pass learning approach, which only needs to scan each instance once and does not need to store the whole data, and thus satisfies the evolving streaming data nature. After tackling this problem in one-shot scenario, we then extend it to multi-shot case. Empirical study on a broad range of data sets shows that our approach can address this problem effectively.

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Semi-supervised one-pass multi-view learning

Cited by 6 publications

References 30 publications

Semi-supervised learning models for document classification: A systematic review and meta-analysis

Semi-supervised learning models for document classification: A systematic review and meta-analysis

Online Bayesian max-margin subspace learning for multi-view classification and regression

One-Pass Learning with Incremental and Decremental Features

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