A Graph-Based Approach for Active Learning in Regression

Zhang, Hongjing; Ravi, S. S.; Davidson, Ian

doi:10.1137/1.9781611976236.32

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…To show that EMOC [46] is the best active learning method for our filter, we replace EMOC with other label-independent active learning methods for regression: GS [50], k-medoids [50], EGAL [51], EMC [52], variance [46], and GBA [57], and the results are shown in Figure 8. While the difference in scores between EMOC, GS, k-medoids, variance, and GBA is small at the 30th iteration, EMOC outperforms other methods in early iterations; therefore, we conclude that EMOC is the best for our purpose.…”

Section: ) Active Learning Methodmentioning

confidence: 99%

“…After selecting L ′ pixels n 1 , ..., n L ′ , we can find the next pixel to be selected, n L ′ +1 , using the active learning method. Among existing active learning methods for regression [46], [48]- [57], we can use only label-independent methods, because the labels p n1 , ..., p n L are changed during the optimization process. We experimentally find that Kading et al's [46] method outperforms other available methods; therefore, we use it in our method.…”

Section: A Active Learning Based Local Filter 1) Parameter Prediction...mentioning

confidence: 99%

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Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter

Kosugi

Yamasaki

2023

IEEE Trans. Circuits Syst. Video Technol.

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In this study, we address local photo enhancement to improve the aesthetic quality of an input image by applying different effects to different regions. Existing photo enhancement methods are either not content-aware or not local; therefore, we propose a crowd-powered local enhancement method for content-aware local enhancement, which is achieved by asking crowd workers to locally optimize parameters for image editing functions. To make it easier to locally optimize the parameters, we propose an active learning based local filter. The parameters need to be determined at only a few key pixels selected by an active learning method, and the parameters at the other pixels are automatically predicted using a regression model. The parameters at the selected key pixels are independently optimized, breaking down the optimization problem into a sequence of single-slider adjustments. Our experiments show that the proposed filter outperforms existing filters, and our enhanced results are more visually pleasing than the results by the existing enhancement methods. Our source code and results are available at https://github.com/satoshi-kosugi/crowd-powered.

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Section: ) Active Learning Methodmentioning

confidence: 99%

Section: A Active Learning Based Local Filter 1) Parameter Prediction...mentioning

confidence: 99%

Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter

Kosugi

Yamasaki

2023

IEEE Trans. Circuits Syst. Video Technol.

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“…, then selects the next evaluation point that maximizes Q(x i ) when x i ∈ U is added to the training set (Z) [31]:…”

Section: ) Sequential Laplacian Regularized V-optimal (Slrv)mentioning

confidence: 99%

“…However as the name suggests, these two approaches do not consider the model uncertainty and only select the samples greedily, which may result in high predictive variance (discussed in Section IV). Recently, Zhang et al propose a graph based active learning (GBAL) approach to select the next evaluation point based on the uncertainty information using L 1 measure, which enables it to use any surrogate model [31].…”

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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“…The most popular active learning sample selection strategies include random sampling and committee voting query (Kee et al, 2018; Zhao et al, 2018). These strategies mainly focus on classification and clustering (Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Gaussian mixture model sample selection strategy–based active semi-supervised soft sensor for industrial processes

Luo,

Lei,

Wang

2023

Transactions of the Institute of Measurement and Control

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Soft sensors have become reliable tools for estimating difficult-to-measure target variables in modern industrial processes. In order to make full use of labeled and unlabeled samples, an active semi-supervised soft sensor modeling method is proposed, which combines active learning and semi-supervised learning to maximize model performance and minimize the laboratory analysis cost of expanding the labeled sample data set. First, manifold regularization is introduced into the deep extreme learning machine (DELM) algorithm to form a semi-supervised DELM that improves the performance of a model trained with unlabeled samples. Then, considering non-Gaussian processes and the error information between the predicted and true values, an active sample selection strategy based on error Gaussian mixture model is developed. Using this strategy, the most uncertain and representative unlabeled samples are selected for labeling, and thereby expanding the labeled sample data set. Finally, the effectiveness of the proposed method is verified using industrial debutanizer process data.

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A Graph-Based Approach for Active Learning in Regression

Cited by 7 publications

References 24 publications

Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter

Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter

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

Gaussian mixture model sample selection strategy–based active semi-supervised soft sensor for industrial processes

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