Distributed Semi-Supervised Metric Learning

Shen, Pengcheng; Du, Xin; Li, Chunguang

doi:10.1109/access.2016.2632158

Cited by 17 publications

(14 citation statements)

References 25 publications

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“…While Laplacian regularization had achieved good results, the solution of Laplacian method was biased towards a constant and lacked of extrapolating power [34]. Regularization based on estimation of the Hessian favors mapping functions whose values vary linearly along the geodesic distance and it preserves the local manifold structure better than Laplacian.…”

Section: B Lhrss-elm Formulationmentioning

confidence: 99%

“…Manifold regularization tried to extract the geometry structure information in the input data space. Laplacian regularization is one of the most popular manifold regularization, utilizing graph Laplacian to determine the geometry of the underlying manifold, has been successful used in semi-supervised tasks [19], [34], [35], [37], [38]. However, if only a few of labeled data available that the performance will be worsen due to lacking of extrapolating power, biased the solution towards a constant function and cannot preserve the local topology architecture [26], [25].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Regularization Semi-Supervised Extreme Learning Machine Method and Its Application

et al. 2019

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Semi-supervised extreme learning machine (SS-ELM) has been applied to many classification and regression assignments with high performance, in which both the labeled and unlabeled data are exploited to enhance accuracy and computation efficiency. The Laplacian manifold regularization method has been incorporated to explore the geometry of the underlying manifold structure. However, the Laplacian manifold regularization lacks the extrapolating ability and biases the solution to a real constant function. In this paper, we propose a novel algorithm, the Laplacian-Hessian regularization SS-ELM (LHRSS-ELM), to enhance the performance of conventional SS-ELM. The main advantages of LHRSS-ELM are as follows: 1) LHRSS-ELM exhibits the learning capability and computational efficiency of traditional SS-ELMs; 2) LHRSS-ELM algorithm combines both Laplacian and Hessian term to enhance the extrapolating power, accuracy, and robustness and also show significant performance in multiclass classification tasks; and 3) for the purpose of pursuing the best pair of hyperparameters to establish a comparable model, we dynamically update them from sequences. The proposed algorithm is evaluated on publicly available data sets and further applied for the state classification of superheating degree in the aluminum electrolysis process. The experimental results demonstrate that the proposed mechanism is superior to the existing state-of-the-art semi-supervised learning algorithms in the matter of accuracy and robustness.

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Section: B Lhrss-elm Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Regularization Semi-Supervised Extreme Learning Machine Method and Its Application

et al. 2019

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“…In this paper, we focus on the issues above and attempt to utilize the idea of metric learning to measure the relationships between users and users, items and items as an additional compensation for implicit collaborative filtering. Metric learning is usually applied on multimedia area, such as image and video [7]. And traditional metric learning is designed to measure the relationship between users and items only, which cannot meet the requirement of implicit collaborative filtering.…”

Section: B Outlines Of This Workmentioning

confidence: 99%

“…Metric learning is a research spot in recent years, as in image recolonization, clustering and recommendation system [7]- [17]. Metric learning is delighted by the inner product, which is a traditional way to measure the relationships between different entities.…”

Section: Related Work a Metric Learningmentioning

confidence: 99%

Improving Recommendations by Embedding Multi-Entity Relationships With Latent Dual-Metric Learning

Yang

Han

et al. 2019

IEEE Access

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Recently, latent vector embedding has become a research hotspot, with its great representative ability to measure the latent relationships among different views. However, most researches utilize the inner product of latent vectors as the representation of relationships, and they develop some embedding models based on this theory. In this paper, we take deep insight into the existing embedding models and find that utilizing the inner product may increase several problems: 1) in latent space, the inner product among three vectors may violate triangle principle; 2) the inner product cannot measure the relationships between vectors in the same category, such as user and user and item and item; and 3) the inner product cannot catch the collaborative relationships (user-user and item-item) for collaborative filtering. Along with this line, we propose a latent vector embedding model for collaborative filtering: latent dual metric embedding (LDME), which utilizes the dual-Euclidean distance in latent space, instead of the inner product, to represent different types of relationships (user-user, item-item, and user-item) with a uniform framework. Specifically, we design an embedding loss function in LDME, which can measure the close and remote relationships between entities, tackle the above problems, and achieve a more clear, well-explained embedding result. Extensive experiments are conducted on several real-world datasets (Amazon, Yelp, Taobao, and Jingdong), where the expiring results demonstrate that LDME can overperform some state-of-the-art user-item embedding models and can benefit the existing collaborative filtering models. INDEX TERMS Latent vector embedding, metric learning, collaborative filtering, recommender system.

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“…Later, semi supervised learning algorithms based on manifold regularization have been widely utilized to effectively exploit the information from unevaluated points [49]- [53]. Laplacian regularization is one of the popular manifold regularization techniques that uses graph Laplacian to determine the information of underlying manifold [54]- [57]. Laplacian regularization has been successfully applied to many classification and regression problems [58]- [61].…”

Section: Introductionmentioning

confidence: 99%

An Active Learning Methodology for Efficient Estimation of Expensive Noisy Black-Box Functions Using Gaussian Process Regression

et al. 2020

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Estimation of black-box functions often requires evaluating an extensive number of expensive noisy points. Learning algorithms can actively compare the similarity between the evaluated and unevaluated points to determine the most informative subsequent points for efficient estimation of expensive functions in a sequential procedure. In this paper, we propose an active learning methodology based on the integration of Laplacian regularization and active learning-Cohn (ALC) measure for identification of the most informative points for efficient estimation of noisy black-box functions using Gaussian processes. We propose two simple greedy search algorithms for sequential optimization of the tuning parameters and determination of subsequent points based on the information from the previously evaluated points. We also enhance the graph Laplacian with the information of both the predictor and response variables to capture the similarity between the points more effectively. The proposed methodology is particularly suited for problems involving estimation of expensive black-box functions with a high level of noise and plenty of unevaluated points. Using a case study for analysis of the kinematics of pitching in baseball as well as simulation experiments, we demonstrate the performance of the proposed methodology against existing methods in the literature in terms of estimation error.

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Distributed Semi-Supervised Metric Learning

Cited by 17 publications

References 25 publications

A Hybrid Regularization Semi-Supervised Extreme Learning Machine Method and Its Application

A Hybrid Regularization Semi-Supervised Extreme Learning Machine Method and Its Application

Improving Recommendations by Embedding Multi-Entity Relationships With Latent Dual-Metric Learning

An Active Learning Methodology for Efficient Estimation of Expensive Noisy Black-Box Functions Using Gaussian Process Regression

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