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selective sampling with Gromov–Hausdorff metric:Efficient dense-shape correspondence via Confidence-based sample consensus

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虚拟现实与智能硬件（中英文） 2024年第1期6卷 30-42页

作者： Dvir GINZBURG Dan RAVIV Department of Engineering University of Tel AvivIsrael

Background Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” scenario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resulting in slow convergence, high computational costs, and learning failures, particularly when small datasets are used. Methods A novel method is presented for dense-shape correspondence, whereby the spatial information transformed by neural networks is combined with the projections onto spectral maps to overcome the “chicken or egg” challenge by selectively sampling only points with high confidence in their alignment. These points then contribute to the alignment and spectral loss terms, boosting training, and accelerating convergence by a factor of five. To ensure full unsupervised learning, the Gromov–Hausdorff distance metric was used to select the points with the maximal alignment score displaying most confidence. Results The effectiveness of the proposed approach was demonstrated on several benchmark datasets, whereby results were reported as superior to those of spectral and spatial-based methods. Conclusions The proposed method provides a promising new approach to dense-shape correspondence, addressing the key challenges in the field and offering significant advantages over the current methods, including faster convergence, improved accuracy, and reduced computational costs.

关键词： Dense-shape correspondence Spatial information Neural networks Spectral maps selective sampling

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selective sampling for Sensor Type Classification in Buildings 19

Selective Sampling for Sensor Type Classification in Buildin...

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19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

作者： Ma, Jing Hong, Dezhi Wang, Hongning Univ Virginia Charlottesville VA 22904 USA Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781728154978

A key barrier to applying any smart technology to a building is the requirement of locating and connecting to the necessary resources among the thousands of sensing and control points, i.e., the metadata mapping problem. Existing solutions depend on exhaustive manual annotation of sensor metadata - a laborious, costly, and hardly scalable process. To reduce the amount of manual effort required, this paper presents a multi-oracle selective sampling framework to leverage noisy labels from information sources with unknown reliability such as existing buildings, which we refer to as weak oracles, for metadata mapping. This framework involves an interactive process, where a small set of sensor instances are progressively selected and labeled for it to learn how to aggregate the noisy labels as well as to predict sensor types. Two key challenges arise in designing the framework, namely, weak oracle reliability estimation and instance selection for querying. To address the first challenge, we develop a clustering-based approach for weak oracle reliability estimation to capitalize on the observation that weak oracles perform differently in different groups of instances. For the second challenge, we propose a disagreement-based query selection strategy to combine the potential effect of a labeled instance on both reducing classifier uncertainty and improving the quality of label aggregation. We evaluate our solution on a large collection of real-world building sensor data from 5 buildings with more than 11, 000 sensors of 18 different types. The experiment results validate the effectiveness of our solution, which outperforms a set of state-of-the-art baselines.

关键词： selective sampling label aggregation sensor type classification smart buildings

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selective sampling and Optimal Filtering for Subpixel-Based Image Down-sampling

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IEEE ACCESS 2019年 7卷 124096-124105页

作者： Chae, Sung-Ho Kim, Sung-Tae Kim, Joon-Yeon Yoo, Cheol-Hwan Ko, Sung-Jea Korea Univ Sch Elect Engn Seoul 02841 South Korea

Subpixel-based image down-sampling has been widely used to improve the apparent resolution of down-sampled images on display. However, previous subpixel rendering methods often introduce distortions, such as aliasing and color-fringing. This study proposes a novel subpixel rendering method that uses selective sampling and optimal filtering. We first generalize the previous frequency domain analysis results indicating the relationships between various down-sampling patterns and the aliasing artifact. Based on this generalized analysis, a subpixel-based down-sampling pattern for each image is selectively determined by utilizing the edge distribution of the image. Moreover, we investigate the origin of the color-fringing artifact in the frequency domain. Optimal spatial filters that can effectively remove distortions caused by the selected down-sampling pattern are designed via frequency domain analyses of aliasing and color-fringing. The experimental results show that the proposed method is not only robust to the aliasing and color-fringing artifacts but also outperforms the existing ones in terms of information preservation.

关键词： Aliasing color-fringing frequency domain analysis image down-sampling optimal filtering selective sampling subpixel rendering

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selective sampling for trees and forests

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NEUROCOMPUTING 2019年 358卷 93-108页

作者： Badarna, Murad Shimshoni, Ilan Univ Haifa Fac Social Sci Dept Informat Syst Haifa Israel

In this paper we describe selective sampling algorithms for decision trees and random forests and their contribution to the classification accuracy. In our selective sampling algorithms, the instance that yields the highest expected utility is chosen to be labeled by the expert. We show that it is possible to obtain the most valuable unlabeled instance to be labeled by the expert and added to the training dataset of the decision tree simply by depicting the influence of this new instance on the class probabilities of the leaves. All the unlabeled instances that fall into the same leaf will have the same class probabilities. As a result, we can compute the expected accuracy of the decision tree according to its leaves instead for each individual unlabeled instance. An extension for random forests is also presented. Moreover, we show that the selective sampling classifier has to belong to the same family as the classifier whose accuracy we wish to improve but need not be identical to it. For example, a random forest classifier can be used for the selective sampling process, and the results can be used to improve the classification accuracy of a decision tree. Likewise, a random forest classifier consisting of three trees can be used in the selective sampling algorithm to improve the classification accuracy of a random forest consisting of ten trees. Our experiments show that the proposed selective sampling algorithms achieve better accuracy than the standard random sampling, uncertainty sampling and the active belief decision tree learning approach (ABC4.5) for several real-world datasets. We also show that our selective sampling algorithms improve significantly the classification performance of several state-of-the-art classifiers such as the random rotation forest classifier for real-world large-scale datasets. (C) 2019 Published by Elsevier B.V.

关键词： selective sampling Decision trees Random forests Classification Active learning

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Reasoning mechanism: An effective data reduction algorithm for on-line point cloud selective sampling of sculptured surfaces

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COMPUTER-AIDED DESIGN 2019年 113卷 48-61页

作者： Li, Yan Liu, Haibo Tao, Ye Liao, Jianxing Sichuan Univ Sch Mfg Sci & Engn 24 South Sect 1Yihuan Rd Chengdu 610065 Sichuan Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China Energy Res Ctr Lower Saxony EFZN D-38640 Goslar Germany

For obtaining a high-quality profile of measured sculptured surface, scanning devices have to produce massive point cloud data with great sampling rates. Bottlenecks are created owing to inefficiencies in storing, manipulating and transferring these data, and the parametric modeling from them is quite a time-consuming work. The purpose of this paper is to effectively simplify point cloud data from a measured sculptured surface during the on-line point cloud data selective sampling process. The key contribution is the generation of a novel reasoning mechanism which is based on a predictor-corrector scheme, and it is capable of eliminating data redundancy caused by spatial similarity of collected point clouds. In particular, this mechanism is embedded in our newly designed framework for on-line point cloud data selective sampling of sculptured surfaces. This framework consists of two stages: First, the initial point data flow is selective sampled using bi-Akima method;second, the data flow is refined based on our proposed reasoning mechanism. Moreover, our versatile framework is capable of obtaining high-quality resampling results with smaller data reduction ratio than other existing online point cloud data reduction/selective sampling methods. Experiment is conducted and the results demonstrate the superior performance of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Sculptured surface Reasoning mechanism Data reduction Point cloud selective sampling

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Extended pre-processing pipeline for text classification: On the role of meta-feature representations, sparsification and selective sampling

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INFORMATION PROCESSING & MANAGEMENT 2020年第4期57卷 102263-102263页

作者： Cunha, Washington Canuto, Sergio Viegas, Felipe Salles, Thiago Gomes, Christian Mangaravite, Vitor Resende, Elaine Rosa, Thierson Goncalves, Marcos Andre Rocha, Leonardo Univ Fed Minas Gerais Belo Horizonte MG Brazil Univ Fed Sao Joao del Rei So Joao Del Rei MG Brazil Univ Fed Goias Aparecida De Goiania Go Brazil

Text Classification pipelines are a sequence of tasks needed to be performed to classify documents into a set of predefined categories. The pre-processing phase (before training) of these pipelines involve different ways of transforming and manipulating the documents for the next (learning) phase. In this paper, we introduce three new steps into the pre-processing phase of text classification pipelines to improve effectiveness while reducing the associated costs. The distance-based Meta-Features (MFs) generation step aims at reducing the dimensionality of the original termdocument matrix while producing a potentially more informative space that explicitly exploits discriminative labeled information. The second step is a sparsification one aimed at making the MF representation less dense to reduce training costs and noise. The third step is a selective sampling (SS) aimed at removing lines (documents) of the matrix obtained in the previous step, by carefully selecting the "best" documents for the learning phase. Our experiments show that the proposed extended pre-processing pipeline can achieve significant gains in effectiveness when compared to the original TF-IDF (up to 52%) and embedding-based representations (up to 46%), at a much lower cost (up to 9.7x faster in some datasets). Other main contributions of our work include a thorough and rigorous evaluation of the trade-offs between cost and effectiveness associated with the introduction of these new steps into the pipeline as well as a comprehensive comparative experimental evaluation of many alternatives in terms of representations, approaches, etc.

关键词： Text classification pipelines Pre-processing Meta-features selective sampling Sparsification Experimental evaluation

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Matrix Completion with selective sampling 13

Matrix Completion with Selective Sampling

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13th International Conference on sampling Theory and Applications (SampTA)

作者： Parkinson, Christian Huynh, Kevin Needell, Deanna Univ Calif Los Angeles Math Los Angeles CA 90095 USA Univ Calif Los Angeles Comp Sci Los Angeles CA USA

ISBN: (纸本)9781728137414

Matrix completion is a classical problem in data science wherein one attempts to reconstruct a low-rank matrix while only observing some subset of the entries. Previous authors have phrased this problem as a nuclear norm minimization problem. Almost all previous work assumes no explicit structure of the matrix and uses uniform sampling to decide the observed entries. We suggest methods for selective sampling in the case where we have some knowledge about the structure of the matrix and are allowed to design the observation set.

关键词： Matrix completion nuclear-norm minimization selective sampling

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The application of molecularly imprinted polymers in passive sampling for selective sampling perfluorooctanesulfonic acid and perfluorooctanoic acid in water environment

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2018年第33期25卷 33309-33321页

作者： Cao, Fengmei Wang, Lei Ren, Xinhao Wu, Fengchang Sun, Hongwen Lu, Shaoyong Chinese Res Inst Environm Sci State Environm Protect Key Lab Lake Pollut Contro State Key Lab Environm Criteria & Risk Assessment Dayangfang Beiyuan Rd 8 Beijing 100012 Peoples R China Nankai Univ Tianjin Key Lab Environm Remediat & Pollut Contro Key Lab Pollut Proc & Environm Criteria Minist EducColl Environm Sci & Engn Tianjin 300071 Peoples R China Shanxi Univ Sci & Technol Sch Environm Sci & Engn Xian 710021 Shaanxi Peoples R China

Modeling and predicting of a novel polar organic chemical integrative sampler (POCIS) for sampling of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) using molecularly imprinted polymers (MIPs) as receiving phase are presented in this study. Laboratory microcosm experiments were conducted to investigate the uptake kinetics, effects of flow velocity, pH, and dissolved organic matter (DOM), and also the selectivity of the POCIS. In this study, uptake study of PFOA and PFOS sampling on MIP-POCIS, over 14days, was investigated. Laboratory calibrations of MIP-POCIS yielded sampling rate (R-s) values for PFOA and PFOS were 0.387 and 0.229L/d, higher than POCIS using commercial sorbent WAX as receiving phase (0.133 and 0.141L/d for PFOA and PFOS, respectively) in quiescent condition. The R-s values for PFOA and PFOS sampling on MIP-POCIS were increased to 0.591 and 0.281L/d in stirred condition (0.01m/s), and no significant increase occurred when the flow velocity was further increased. The R-s values were kept relatively high in the solution of which the pH was lower than the isoelectric point (IEP) of MIP-sorbent and decreased when the solution pH was extend the IEP value. Under the experimental conditions, DOM seemed to slightly facilitate the R-s values of PFOA and PFOS in MIP-POCIS. The results showed that the interaction between the target compounds and the receiving phase was fully integrated by the imprinting effects and also the electrostatic interaction. Finally, comparing the sampling rate of WAX-POCIS and the MIP-POCIS, the MIP-POCIS offers promising perspectives for selective sampling ability for PFOA and PFOS.

关键词： POCIS Molecularly imprinted polymers sampling rate selective sampling Typical perfluoroalkyl substance

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Parallel rule-based selective sampling and on-demand learning to rank

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第18期31卷

作者： Freitas, Mateus F. e Sousa, Daniel X. Martins, Wellington S. Rosa, Thierson C. Silva, Rodrigo M. Goncalves, Marcos A. Univ Fed Goias Inst Informat Caixa Postal 131 BR-74001970 Goiania Go Brazil Univ Fed Minas Gerais Dept Ciencia Comp Belo Horizonte MG Brazil

Learning to rank (L2R) works by constructing a ranking model from training data so that, given a new query, the model is able to generate an effective rank of the objects for the query. Almost all work in L2R focus on ranking accuracy leaving performance and scalability overlooked. However, performance is a critical factor, especially when dealing with on-demand queries. In this scenario, Learning to Rank using association rules has been shown to be extremely effective but only at a high computational cost. In this work, we show how to exploit parallelism on rule-based systems to: i) drastically reduce L2R training datasets using selective sampling and ii) to generate query customized ranking models on the fly. We present parallel algorithms and GPU implementations for these two tasks showing that dataset reduction takes only a few seconds with speedups up to 148x over a serial baseline, and that queries can be processed in only a few milliseconds with speedups of 1000x over a serial baseline and 29x over a parallel baseline for the best case. We also extend the implementations to work with multiple GPUs, further increasing the speedup over the baselines and showing the scalability of our proposed algorithms.

关键词： association rules learning to rank parallelism selective sampling

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selective sampling and Otsu Algorithm based Spectrum Sensing in Cognitive Radio Networks 25

Selective Sampling and Otsu Algorithm based Spectrum Sensing...

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25th Signal Processing and Communications Applications Conference (SIU)

作者： Namdar, Mustafa Kocak, Fatih Basgumus, Arif Dumlupinar Univ Elekt Elekt Muhendisligi Kutahya Turkey

ISBN: (纸本)9781509064946

In this study, the performance analysis of the spectrum sensing in cognitive radio networks is investigated with the energy detector method over Rician fading channels with the selective sampling and Otsu algorithm. Here, a new method based on noise reduction using the wavelet transform is proposed. Performance analysis of the proposed system model over Rician fading channels is presented by receiver operating characteristic curves for different signal to noise ratio values. The results show that the spectrum sensing performance increases when the selective sampling and Otsu algorithm are used at the receiver.

关键词： cognitive radio wavelet transform energy detector selective sampling Otsu algorithm spectrum sensing

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