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Journal articles on the topic 'Online dictionary learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Traoré, Abraham, Maxime Berar, and Alain Rakotomamonjy. "Online multimodal dictionary learning." Neurocomputing 368 (November 2019): 163–79. http://dx.doi.org/10.1016/j.neucom.2019.08.053.

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2

Wu, Sheng, Ancong Wu, and Wei-Shi Zheng. "Online deep transferable dictionary learning." Pattern Recognition 118 (October 2021): 108007. http://dx.doi.org/10.1016/j.patcog.2021.108007.

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3

Wang, Ying. "Optimization of English Online Learning Dictionary System Based on Multiagent Architecture." Complexity 2021 (April 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/1994121.

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As a universal language in the world, English has become a necessary language communication tool under the globalization of trade. Intelligent, efficient, and reasonable English language-assisted learning system helps to further improve the English ability of language learners. English online learning dictionary, as an important query tool for English learners, is an important part of English online learning. This paper will optimize the design of English online learning dictionary system based on multiagent architecture. Based on the hybrid multiagent cooperative algorithm, this paper will improve the disadvantages of the online English learning dictionary system and propose an appropriate dictionary application evaluation function. At the same time, an improved reinforcement learning algorithm is introduced into the corresponding English online learning dictionary navigation problem so as to improve the efficiency of the online English learning dictionary system. English online learning dictionary is more intelligent and efficient. In this paper, the new online learning dictionary system optimization algorithm is proposed and compared with the traditional system algorithm. The experimental results show that the algorithm proposed in this paper solves the collaborative confusion problem of English learning online dictionary to a certain extent and further solves the corresponding navigation problem so as to improve the efficiency.

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4

Gao, Wei, Jie Chen, Cedric Richard, and Jianguo Huang. "Online Dictionary Learning for Kernel LMS." IEEE Transactions on Signal Processing 62, no. 11 (2014): 2765–77. http://dx.doi.org/10.1109/tsp.2014.2318132.

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5

Naderahmadian, Yashar, Soosan Beheshti, and Mohammad Ali Tinati. "Correlation Based Online Dictionary Learning Algorithm." IEEE Transactions on Signal Processing 64, no. 3 (2016): 592–602. http://dx.doi.org/10.1109/tsp.2015.2486743.

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6

Dziemianko, Anna. "Smart advertising and online dictionary usefulness." International Journal of Lexicography 33, no. 4 (2020): 377–403. http://dx.doi.org/10.1093/ijl/ecaa017.

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Abstract Today, online advertisements are smart. Based on targeting, they are highly personalized to match the interests of Internet users. Smart advertisements feature even in high-quality online dictionaries, which is a trade-off for free dictionary content. The aim of the paper is to investigate the influence of targeted advertisements in online dictionaries on language reception, production and learning. The study also identifies the effect of advertisement targeting on the time of online dictionary consultation. In addition, dictionary users’ attitudes to advertisements are explored. The results reveal that either targeted or non-targeted advertisements in online dictionaries do not significantly affect language reception, production and learning, irrespective of sense position in entries. Yet, both targeted and non-targeted advertisements prolong dictionary consultation. Non-targeted advertisements were found only marginally more disruptive than targeted ones, and the assessment of advertisements was not dependent on the experimental condition.

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Song, Xiaorui, and Lingda Wu. "A Novel Hyperspectral Endmember Extraction Algorithm Based on Online Robust Dictionary Learning." Remote Sensing 11, no. 15 (2019): 1792. http://dx.doi.org/10.3390/rs11151792.

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Due to the sparsity of hyperspectral images, the dictionary learning framework has been applied in hyperspectral endmember extraction. However, current endmember extraction methods based on dictionary learning are not robust enough in noisy environments. To solve this problem, this paper proposes a novel endmember extraction approach based on online robust dictionary learning, termed EEORDL. Because of the large scale of the hyperspectral image (HSI) data, an online scheme is introduced to reduce the computational time of dictionary learning. In the proposed algorithm, a new form of the objective function is introduced into the dictionary learning process to improve the robustness for noisy HSI data. The experimental results, conducted with both synthetic and real-world hyperspectral datasets, illustrate that the proposed EEORDL outperforms the state-of-the-art approaches under different signal-to-noise ratio (SNR) conditions, especially for high-level noise.

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Ambarwati, Rosita, and Berlinda Mandasari. "THE INFLUENCE OF ONLINE CAMBRIDGE DICTIONARY TOWARD STUDENTS’ PRONUNCIATION AND VOCABULARY MASTERY." Journal of English Language Teaching and Learning 1, no. 2 (2020): 50–55. http://dx.doi.org/10.33365/jeltl.v1i2.605.

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Dictionary has become one important tool for learning a foreign language. Many kinds of dictionaries can be used for students who want to learn a foreign language. An online dictionary is one of the technologies that can be used for learning a foreign language. Nowadays in the digital era, students can use it through internet networking. Thus, this study discusses the influence of the online Cambridge dictionary on students' pronunciation and vocabulary of students at Universitas Teknokrat Indonesia (UTI). Subjects of the research were 25 students who take Academic Writing subject majoring in English Education study program. Data were then analyzed by using descriptive analysis. Data were collected through questionnaires and interviews, the student was given a questionnaire and interview related to an alternative reference in technology for learning vocabulary and pronunciation especially online Cambridge dictionary. As a result of this study, the online Cambridge dictionary influences student pronunciation and vocabulary in terms of pronunciation mastery and vocabulary enrichment. It is suggested that the online Cambridge dictionary can be an alternative solution to solve the problem of pronunciation and vocabulary mastery.

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9

Ülkü, İrem, and Ersin Kizgut. "Lossy Compressive Sensing Based on Online Dictionary Learning." Computing and Informatics 38, no. 1 (2019): 151–72. http://dx.doi.org/10.31577/cai_2019_1_151.

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10

Liu, Na, Hong Huo, and Tao Fang. "Robust object tracking via constrained online dictionary learning." Multimedia Tools and Applications 78, no. 3 (2017): 3689–703. http://dx.doi.org/10.1007/s11042-017-5538-4.

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11

Zhou, Tao, Fanghui Liu, Harish Bhaskar, Jie Yang, Huanlong Zhang, and Ping Cai. "Online discriminative dictionary learning for robust object tracking." Neurocomputing 275 (January 2018): 1801–12. http://dx.doi.org/10.1016/j.neucom.2017.10.019.

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12

Zhao, Jingtao, Caixia Yu, Suping Peng, and Zongnan Chen. "Online dictionary learning method for extracting GPR diffractions." Journal of Geophysics and Engineering 16, no. 6 (2019): 1116–23. http://dx.doi.org/10.1093/jge/gxz081.

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Abstract Diffractions in a Ground-Penetrating Radar (GPR) data carry significant responses from near-surface small-scale fractures or karsts. However, this geological information is generally difficult to extract because of the shielding effect of strong reflections from subsurface layers. In order to solve this problem, a GPR diffraction extraction method is proposed for individually separating and imaging of GPR diffractions that incorporates a local plane-wave destruction filter with an online dictionary learning algorithm. The strong reflections are estimated and eliminated by the local plane-wave destruction method and the weak GPR diffractions are extracted by a sparse coding algorithm. In solving this model, a trust-region algorithm is used for accelerating the sparse coding procedures that can scale up gracefully to a large GPR data processing. A numerical experiment demonstrates the good performance of the proposed method in destroying strong reflections and enhancing weak diffractions from small-scale void holes. Real data application further verifies its potential value in resolving fine details of subsurface small-scale buried targets, such as pipes or void holes.

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13

Lu, Yanfei, Rui Xie, and Steven Y. Liang. "Adaptive online dictionary learning for bearing fault diagnosis." International Journal of Advanced Manufacturing Technology 101, no. 1-4 (2018): 195–202. http://dx.doi.org/10.1007/s00170-018-2902-0.

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14

Sun, Yipeng, Mai Xu, Xiaoming Tao, and Jianhua Lu. "Online Dictionary Learning Based Intra-frame Video Coding." Wireless Personal Communications 74, no. 4 (2014): 1281–95. http://dx.doi.org/10.1007/s11277-013-1577-y.

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15

Fu, Wei, Shutao Li, Leyuan Fang, and Jon Atli Benediktsson. "Contextual Online Dictionary Learning for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing 56, no. 3 (2018): 1336–47. http://dx.doi.org/10.1109/tgrs.2017.2761893.

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16

Bhanot, Argheesh, Celine Meillier, Fabrice Heitz, and Laura Harsan. "Spatially Constrained Online Dictionary Learning for Source Separation." IEEE Transactions on Image Processing 30 (2021): 3217–28. http://dx.doi.org/10.1109/tip.2021.3058558.

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17

Fauzi, Andi Rizki. "The students’ responses on integrating online corpus and online dictionary to expand English vocabulary for tourism." UAD TEFL International Conference 2 (January 18, 2021): 186. http://dx.doi.org/10.12928/utic.v2.5756.2019.

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Vocabulary is considered as the most essential part for students in English learning including those who are learning English for Tourism. The reason behind this is that nothing can be conveyed both in written or spoken language without vocabulary. Some researchers attempted to apply some strategies and approaches to assist the students in gaining vocabulary mastery. One of the ways is by using a corpus-based approach. However, to understand the corpus data need huge effort if the students have a lack of vocabulary due to the complexity of the language used. For that reason, the use of the online dictionary is required that eases the students to seek the meaning of unfamiliar words in the corpus. The researcher aims to know the students’ responses on integrating online corpus and dictionary to expand English vocabulary for tourism after conducting experimental research to the fourth semester of hospitality students in 8 meetings. In the treatments given, the researcher only focused on 100 words which were considered as unfamiliar words selected from articles in tourism fields. The questionnaires were distributed to recognize what they perceived after the integration of online corpus and the online dictionary. The result showed that the students responded positively to the use of the online dictionary while learning vocabulary through online corpus as the students could understand the corpus data presented better than without the use of online dictionary and they could broaden their vocabulary.

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18

Fan, Baojie, Yingkui Du, and Yang Cong. "Online Learning Discriminative Dictionary with Label Information for Robust Object Tracking." Abstract and Applied Analysis 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/189317.

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A supervised approach to online-learn a structured sparse and discriminative representation for object tracking is presented. Label information from training data is incorporated into the dictionary learning process to construct a robust and discriminative dictionary. This is accomplished by adding an ideal-code regularization term and classification error term to the total objective function. By minimizing the total objective function, we learn the high quality dictionary and optimal linear multiclassifier jointly using iterative reweighed least squares algorithm. Combined with robust sparse coding, the learned classifier is employed directly to separate the object from background. As the tracking continues, the proposed algorithm alternates between robust sparse coding and dictionary updating. Experimental evaluations on the challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness, accuracy, and robustness.

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19

Aidah Fitriya. "PENGARUH MEDIA PEMBELAJARAN KAMUS ONLINE AL-MA’ĀNY DALAM MENINGKATKAN MAHĀRAH AL-QIRĀ’AH PADA SISWA KELAS X MAN 2 SLEMAN TAHUN AJARAN 2017/2018 (Studi Eksperimen)." EDULAB: Majalah Ilmiah Laboratorium Pendidikan 5, no. 1 (2020): 1–19. http://dx.doi.org/10.14421/edulab.2020.51-01.

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This study aims to determine how the application of Al-Ma'any online dictionary media and whether there is a difference between learning that uses Al-Ma'any online dictionary media and those who do not use Al-Ma'any online dictionary media for maharah Al-Qirā'ah in class X MAN 2 Sleman. This study uses a quantitative approach while the type of research is Experimental Research. The sample in this study was 60 students, consisting of class X MIA I which was used as an experimental class with a total of 30 students and class X MIA II which was used as a control class with a total of 30 students. For data collection techniques in this study using conservation, test and documentation. Analysis of the data using T test analysis with analysis requirements Normality Test using Kolmogrof-Smirnov and homogeneity. The results of this study indicate that learning Arabic by using Al-Ma'āny online dictionary learning media can improve the Mahārah of Al-Qirā'ah in the learning of Arabic students. This is demonstrated by using SPSS 19. The output results show that in the Independent Sample Test t-test the posttest value of the experimental class students found that a significant value of 0,000 <0.05 and from the analysis of the T test the value of T counted 5.148> T table 2.045, then H0 is rejected, meaning that there is a difference in the use of Al-Ma'āny's online dictionary learning media for the learning of the Mahārah of Al-Qirā'ah Keywords: Learning Media, Al-Ma’any Dictionary. mahārah Al-Qirā’ah

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20

Dziemianko, Anna. "The role of online dictionary advertisements in language reception, production, and retention." ReCALL 31, no. 01 (2018): 5–22. http://dx.doi.org/10.1017/s0958344018000149.

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AbstractToday, people want to consult dictionaries of good quality, but they expect them to be available online for free. For this reason, publishers need advertisements to cover the costs of producing and maintaining online dictionary content. This paper aims to investigate the role of advertisements in language reception, production, and learning following online dictionary consultation. It also undertakes to determine whether advertisements in an online dictionary affect the time needed for dictionary-based decoding and encoding. In the experiment, participants consulted one of the most prestigious dictionaries of English for foreign learners, OALD9 (Oxford Advanced Learner’s Dictionary, 9th edition). Two versions of the dictionary were employed in the study: one that includes advertisements and is available online free of charge, and its advertisement-free counterpart, OALD9 Premium. In the receptive task, the participants were requested to explain the meaning of new English words. In the productive one, they had to use the words in sentences. Meaning retention was checked in the immediate post-test. The research reveals that advertisements in OALD9 impede language reception, production, and learning, and also extend the time for comprehending new words. Suggestions for further research into advertisements on dictionary websites are presented in the final part of the paper.

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21

Sha, Lingdao, Dan Schonfeld, and Jing Wang. "Graph Regularized Sparse Coding by Modified Online Dictionary Learning." Electronic Imaging 2017, no. 2 (2017): 27–31. http://dx.doi.org/10.2352/issn.2470-1173.2017.2.vipc-402.

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22

Xiang, Yu, Guoqiang Zhang, Shuhang Gu, and Jianrui Cai. "Online multi-layer dictionary pair learning for visual classification." Expert Systems with Applications 105 (September 2018): 174–82. http://dx.doi.org/10.1016/j.eswa.2018.03.048.

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23

Kamal, Ilias, Khalid Housni, and Youssef Hadi. "Online dictionary learning for car recognition using sparse coding and LARS." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 1 (2020): 164. http://dx.doi.org/10.11591/ijai.v9.i1.pp164-174.

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<p>The bag of feature method coupled with online dictionary learning is the basis of our car make and model recognition algorithm. By using a sparse coding computing technique named LARS (Least Angle Regression) we learn a dictionary of codewords over a dataset of Square Mapped Gradient feature vectors obtained from a densely sampled narrow patch of the front part of vehicles. We then apply SVMs (Support Vector Machines) and KMeans supervised classification to obtain some promising results.</p>

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Ging, Debbie, Theodore Lynn, and Pierangelo Rosati. "Neologising misogyny: Urban Dictionary’s folksonomies of sexual abuse." New Media & Society 22, no. 5 (2019): 838–56. http://dx.doi.org/10.1177/1461444819870306.

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Web 2.0 has facilitated a particularly toxic brand of digital men’s rights activism, collectively known as the Manosphere. This amorphous network of online publics is noted for its virulent anti-feminism, extreme misogyny and synergies with the alt-right. Early manifestations of this phenomenon were confined largely to 4/Chan, Reddit and numerous alt-right forums. More recently, however, this rhetoric has become increasingly evident in Urban Dictionary. This article presents the findings of a machine-learning and manual analysis of Urban Dictionary’s entries relating to sex and gender, to assess the extent to which the Manosphere’s discourses of extreme misogyny and anti-feminism are working their way into everyday vernacular contexts. It also considers the sociolinguistic and gender-political implications of algorithmic and linguistic capitalism, concluding that Urban Dictionary is less a dictionary than it is a platform of folksonomies, which may exert a disproportionate and toxic influence on online discourses related to gender and sexuality.

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Ma, Qing. "University L2 Learners' Voices and Experience in Making Use of Dictionary Apps in Mobile Assisted Language Learning (MALL)." International Journal of Computer-Assisted Language Learning and Teaching 9, no. 4 (2019): 18–36. http://dx.doi.org/10.4018/ijcallt.2019100102.

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Language learners can now access language learning information anywhere and anytime with handheld mobile devices connected to unlimited online information. Searching the meaning of unknown language items encountered online may be the first step for language learning to take place. Making good use of e-dictionaries and dictionary apps is a key factor that affects learning efficiency. Using a survey questionnaire completed by more than 200 participants and examining both quantitative and qualitative data, this study investigated how Hong Kong university L2 learners make use of dictionary apps and probed into what dictionary functions they actually use and what functions they desire when they engage in MALL. Four categories of dictionary functions, including lexical information, extra resources, lexical tutors, and lexical tools, are used to describe all dictionary functions that students accessed or desired. The results show that dictionary apps have become essential tools for Hong Kong university students to learn an L2 as well as facilitate their academic studies; bilingual dictionaries with multiple functions and rich resources are meaning decoders that help students engage in learning both in their subject courses and English learning. Some discrepancies exist in learners' actual use and desired use of dictionary functions, which deserve the further attention of both app developers and language teachers to improve learners' efficiency of vocabulary learning. In addition, teachers play an important role in guiding students' lexical learning. Based on the findings, a framework for understanding Hong Kong university students' choice and use of dictionary apps is provided, based on which implications are offered and discussed.

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Al-Shehri, Saleh, and Christina Gitsaki. "Online reading: a preliminary study of the impact of integrated and split-attention formats on L2 students’ cognitive load." ReCALL 22, no. 3 (2010): 356–75. http://dx.doi.org/10.1017/s0958344010000212.

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AbstractCognitive load theory has been utilized by second language acquisition (SLA) researchers to account for differences in learner performance with regards to different learning tasks. Certain instructional designs were shown to have an impact on cognitive load and working memory, and this impact was found to be accentuated in a multimedia environment where there is a variety of interacting elements and tools that can lead to cognitive overload and consequently reduced learning outcomes. This study investigated the influence of split-attention and integrated instructional formats on students’ cognitive load and how they might facilitate second language online reading and vocabulary learning. Twenty English as a Second Language (ESL) intermediate students studying at an Australian language institution were randomly assigned to four conditions: SAND (Split-Attention No Dictionary), SAOD (Split-Attention with Online Dictionary), IFND (Integrated Format No Dictionary), and IFOD (Integrated Format with Online Dictionary). Subjects were asked to complete an online reading comprehension task in which subjects tested under the SAND and SAOD conditions were exposed to a typical reading text followed by comprehension questions, whereas subjects in the IFND and IFOD conditions were exposed to a reading text where the comprehension questions were physically inserted within the reading text. Under the SAOD and IFOD conditions, subjects had access to an online dictionary. Results showed that the integrated reading format was found to facilitate students’ reading comprehension more than the split-attention format. Also, students who had access to the online dictionary in the SAOD and IFOD groups performed better on the vocabulary test, but spent more time on the reading task than the other two groups. Furthermore, participants in the split-attention format group looked up more words than their counterparts in the integrated format group. The results of the present study have certain implications for multimedia instructional designers and ESL teachers.

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27

KolaliKhormuji, Morteza, and Mehdi Sadeghzadeh. "Online Dictionary Learning using Biogeography-based Optimization for Sparse Representation." International Journal of Computer Applications 101, no. 4 (2014): 1–6. http://dx.doi.org/10.5120/17672-8492.

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Long, Zhiying, Lu liu, Zhe Gao, Maoming Chen, and Li Yao. "A semi-blind online dictionary learning approach for fMRI data." Journal of Neuroscience Methods 323 (July 2019): 1–12. http://dx.doi.org/10.1016/j.jneumeth.2019.03.014.

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Jifara, Worku, Feng Jiang, Bing Zhang, et al. "Hyperspectral image compression based on online learning spectral features dictionary." Multimedia Tools and Applications 76, no. 23 (2017): 25003–14. http://dx.doi.org/10.1007/s11042-017-4724-8.

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Irofti, Paul, Florin Stoican, and Vicenç Puig. "Fault handling in large water networks with online dictionary learning." Journal of Process Control 94 (October 2020): 46–57. http://dx.doi.org/10.1016/j.jprocont.2020.08.003.

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Xie, Yuan, Wensheng Zhang, Cuihua Li, Shuyang Lin, Yanyun Qu, and Yinghua Zhang. "Discriminative Object Tracking via Sparse Representation and Online Dictionary Learning." IEEE Transactions on Cybernetics 44, no. 4 (2014): 539–53. http://dx.doi.org/10.1109/tcyb.2013.2259230.

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32

Zheng, Tingyi, and Li Wang. "Using online dictionary learning to improve Bayer pattern image coding." International Journal of Computational Science and Engineering 16, no. 2 (2018): 132. http://dx.doi.org/10.1504/ijcse.2018.090485.

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Zheng, Tingyi, and Li Wang. "Using online dictionary learning to improve Bayer pattern image coding." International Journal of Computational Science and Engineering 16, no. 2 (2018): 132. http://dx.doi.org/10.1504/ijcse.2018.10011522.

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Liu, Jialin, Cristina Garcia-Cardona, Brendt Wohlberg, and Wotao Yin. "First- and Second-Order Methods for Online Convolutional Dictionary Learning." SIAM Journal on Imaging Sciences 11, no. 2 (2018): 1589–628. http://dx.doi.org/10.1137/17m1145689.

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35

Lee, Mihyang, and Saehwan Eum. "The Evaluation of Microstructure of Online Chinese Learning Dictionary - Focus on the 『Naver Chinese Dictionary』." Chinese Language Education and Research 29 (June 30, 2019): 125–47. http://dx.doi.org/10.24285/cler.2019.6.29.125.

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36

Guo, Hai, Jing Ying Zhao, and Xiao Niu Li. "Design and Realization of Dai Writing Online Dictionary." Advanced Materials Research 831 (December 2013): 511–14. http://dx.doi.org/10.4028/www.scientific.net/amr.831.511.

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Dai writing as the product of Dai civilization. Because of the limitation of culture and territory, it cant spread widely. In todays society, the utilization rate of it still very low, only few people are familiar with it and know it. Remain resources still very limited, cant touched by people. This article uses .net platform to develop Dai writing online inquiry system, meeting the learning requirement of people who want to learn Dai writing.

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Yoon, Gang-Joon, Hyeong Hwang, and Sang Yoon. "Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning." Sensors 18, no. 10 (2018): 3513. http://dx.doi.org/10.3390/s18103513.

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Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems. However, visual object tracking faces various challenging issues because tracking is influenced by illumination change, pose change, partial occlusion and background clutter. Sparse representation-based appearance modeling and dictionary learning that optimize tracking history have been proposed as one possible solution to overcome the problems of visual object tracking. However, there are limitations in representing high dimensional descriptors using the standard sparse representation approach. Therefore, this study proposes a structured sparse principal component analysis to represent the complex appearance descriptors of the target object effectively with a linear combination of a small number of elementary atoms chosen from an over-complete dictionary. Using an online dictionary for learning and updating by selecting similar dictionaries that have high probability makes it possible to track the target object in a variety of environments. Qualitative and quantitative experimental results, including comparison to the current state of the art visual object tracking algorithms, validate that the proposed tracking algorithm performs favorably with changes in the target object and environment for benchmark video sequences.

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Zhu, Lingchen, Entao Liu, and James H. McClellan. "Sparse-promoting full-waveform inversion based on online orthonormal dictionary learning." GEOPHYSICS 82, no. 2 (2017): R87—R107. http://dx.doi.org/10.1190/geo2015-0632.1.

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Full-waveform inversion (FWI) delivers high-resolution images of the subsurface by minimizing iteratively the misfit between recorded and calculated seismic data. We have attacked this misfit successfully with the Gauss-Newton method and sparsity-promoting regularization based on fixed multiscale transforms that permit significant subsampling of the seismic data when the model perturbation at each FWI data-fitting iteration can be represented with sparse coefficients. Rather than using analytical transforms with predefined dictionaries to achieve sparse representation, we developed an adaptive transform called the sparse orthonormal transform (SOT), whose dictionary is learned from many small training patches taken from the model perturbations in previous iterations. The patch-based dictionary is constrained to be orthonormal and trained with an online approach to provide the best sparse representation of the complex features and variations in the entire model perturbation. The complexity of the training method is proportional to the cube of the number of samples in one small patch. By incorporating compressive subsampling and the adaptive SOT-based representation into the Gauss-Newton least-squares problem for each FWI iteration, the model perturbation can be recovered after an [Formula: see text]-norm sparsity constraint is applied on the SOT coefficients. Numerical experiments on synthetic models determined that the SOT-based sparsity-promoting regularization can provide robust FWI results with reduced computation.

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Song, Xiaorui, and Lingda Wu. "Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning." Applied Sciences 9, no. 15 (2019): 3062. http://dx.doi.org/10.3390/app9153062.

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To address the problems of defective pixels and strips in hyperspectral images affecting subsequent processing and applications, we modeled the hyperspectral image (HSI) inpainting problem as a sparse signal reconstruction problem with incomplete observations using the theory of sparse representation, and proposed an HSI inpainting algorithm based on spectral dictionary learning. First, we studied the HSI observation model under the assumption of additive noise. We subsequently proposed a new algorithm for constructing a spectral dictionary directly from hyperspectral data by introducing an online learning optimization method and performing dictionary learning using a robust function. Afterwards, the image was sparsely encoded by applying the variable decomposition and augmented Lagrangian sparse regression method. Finally, the inpainted HSI was obtained by sparse reconstruction. The experimental results showed that compared with the existing algorithms, the algorithm proposed herein could effectively inpaint the defective HSI under different noise conditions with a shorter calculation time than those of existing methods and other dictionary learning inpainting algorithms.

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Hootsen, Geke, Rintse van der Werf, and Anne Vermeer. "E-learning op Maat." In gesprek 78 (January 1, 2007): 119–30. http://dx.doi.org/10.1075/ttwia.78.11hoo.

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The present study explored the effect of reading texts in an electronic learning environment for 46 adult second language learners of Dutch with different proficiency levels. The reading materials were selected dynamically, based on the "fit" between the vocabulary proficiency of individual students and the relative difficulty of the texts. Text difficulty was determined on the basis of the relative proportion of words unknown to the learner (lemma coverage). The texts were analyzed, selected and presented online, accompanied by a personalized electronic dictionary with words that were assumed to be unknown to the reader. The vocabulary learned implicitly while reading was measured in a pretest - treatment - posttest design. The learners read three texts, with lemma coverages of 92%, 90% and 85%, respectively. The relation between dictionary use and word retention was also examined. The study revealed an average word-learning improvement of 32,5%, with no differences between texts with a 92%, 90% or 85% lemma coverage. With respect to dictionary use, the learners looked up an average of 8.0 words. Two-thirds of these words were known to them; they looked up only one-third of the unknown words. We found there was a relation between dictionary use and the retention of unknown words. We concluded that the method we propose for an automated adaptive selection of reading texts and individualized dictionaries ensures that learners of different proficiency levels receive linguistic input that is best suited to their abilities.

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Bai, Jing, Bin Liu, Lei Wang, and Licheng Jiao. "PolSAR image compression based on online sparse K-SVD dictionary learning." Multimedia Tools and Applications 76, no. 23 (2017): 24859–70. http://dx.doi.org/10.1007/s11042-017-4640-y.

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Hong, Tao, and Zhihui Zhu. "Online learning sensing matrix and sparsifying dictionary simultaneously for compressive sensing." Signal Processing 153 (December 2018): 188–96. http://dx.doi.org/10.1016/j.sigpro.2018.05.021.

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Pang, Junbiao, Chunjie Zhang, Lei Qin, et al. "Online dictionary learning for Local Coordinate Coding with Locality Coding Adaptors." Neurocomputing 157 (June 2015): 61–69. http://dx.doi.org/10.1016/j.neucom.2015.01.035.

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Cheng Deqiang, 程德强, 于文洁 Yu Wenjie, 郭昕 Guo Xin, 庄焕东 Zhuang Huandong, and 付新竹 Fu Xinzhu. "Super-Resolution Reconstruction Algorithm Based on Adaptive Image Online Dictionary Learning." Laser & Optoelectronics Progress 57, no. 6 (2020): 061505. http://dx.doi.org/10.3788/lop57.061505.

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Wang, Yi, Qingqing Zheng, and Pheng Ann Heng. "Online Robust Projective Dictionary Learning: Shape Modeling for MR-TRUS Registration." IEEE Transactions on Medical Imaging 37, no. 4 (2018): 1067–78. http://dx.doi.org/10.1109/tmi.2017.2777870.

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Zhang, Xiang, Naiyang Guan, Dacheng Tao, Xiaogang Qiu, and Zhigang Luo. "Online Multi-Modal Robust Non-Negative Dictionary Learning for Visual Tracking." PLOS ONE 10, no. 5 (2015): e0124685. http://dx.doi.org/10.1371/journal.pone.0124685.

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Guo, Min, Hongyan Zhang, Jiayi Li, Liangpei Zhang, and Huanfeng Shen. "An Online Coupled Dictionary Learning Approach for Remote Sensing Image Fusion." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 4 (2014): 1284–94. http://dx.doi.org/10.1109/jstars.2014.2310781.

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Xiang, Fengtao, Zhang Jian, Pan Liang, and Gu Xueqiang. "Robust image fusion with block sparse representation and online dictionary learning." IET Image Processing 12, no. 3 (2018): 345–53. http://dx.doi.org/10.1049/iet-ipr.2017.0327.

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Zhou, Tao, Fanghui Liu, Harish Bhaskar, and Jie Yang. "Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning." IEEE Transactions on Cybernetics 48, no. 9 (2018): 2643–55. http://dx.doi.org/10.1109/tcyb.2017.2747998.

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Li, Jun, Yuanxi Peng, Minghui Song, and Lu Liu. "Image fusion based on guided filter and online robust dictionary learning." Infrared Physics & Technology 105 (March 2020): 103171. http://dx.doi.org/10.1016/j.infrared.2019.103171.

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