• Nem Talált Eredményt

[1] Acharjya, Prasanna Debi, and K Ahmed. A survey on big data analytics: challenges, open research issues and tools. International Journal of Advanced Computer Science and Applications, 7(2):511–518, 2016.

[2] Luigi Atzori, Antonio Iera, and Giacomo Morabito. The internet of things: A survey.

Computer networks, 54(15):2787–2805, 2010.

[3] Marios Bakratsas, Pavlos Basaras, Dimitrios Katsaros, and Leandros Tassiulas.

Hadoop mapreduce performance on ssds for analyzing social networks. Big data re-search, 11:1–10, 2017.

[4] Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput, 15(6):1373–1396, June 2003.

[5] Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen. Pearson correlation coefficient. InNoise reduction in speech processing, pages 1–4. Springer, 2009.

[6] Prodyot K Bhattacharya and Prabir Burman. Theory and methods of statistics. Aca-demic Press, 2016.

[7] S Boyd and L Vandenberghe. Faybusovich convex optimization. IEEE Transactions on Automatic Control, 51(11):1859–1859, 2013.

[8] Peter J Brockwell and Richard A Davis. Introduction to time series and forecasting.

springer, 2016.

[9] Deng Cai, Xiaofei He, and Jiawei Han. Document clustering using locality preserving indexing. IEEE Trans. on Knowl. and Data Eng., 17(12):1624–1637, 2005.

[10] Deng Cai, Chiyuan Zhang, and Xiaofei He. Unsupervised feature selection for multi-cluster data. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’10, pages 333–342, New York, NY, USA, 2010. ACM.

[11] Meng Cai and Jia Liu. Maxout neurons for deep convolutional and LSTM neural networks in speech recognition. Speech Communication, 77:53–64, mar 2016.

[12] Xiuxia Cai, Bin Song, and Zhiqian Fang. Exemplar based Regular Texture Synthesis Using LSTM. Pattern Recognition Letters, sep 2019.

[13] Jian Cao, Zhi Li, and Jian Li. Financial time series forecasting model based on CEEM-DAN and LSTM. Physica A: Statistical Mechanics and its Applications, 519:127–139, 2019.

[14] Tianfeng Chai and Roland R Draxler. Root mean square error (rmse) or mean abso-lute error (mae)? Geoscientific Model Development Discussions, 7:1525–1534, 2014.

[15] Xiaojun Chang, Feiping Nie, Zhigang Ma, Yi Yang, and Xiaofang Zhou. A convex formulation for spectral shrunk clustering. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI’15, pages 2532–2538. AAAI Press, 2015.

[16] Xiaojun Chang, Feiping Nie, Yi Yang, Chengqi Zhang, and Heng Huang. Convex sparse pca for unsupervised feature learning. ACM Trans. Knowl. Discov. Data, 11(1):3:1–3:16, July 2016.

[17] Fan Chung and Graham RK. Spectral graph theory. American Mathematical Soc., 1997.

[18] Karthik C.Lakshmanan, Patrick Sadtler, Elizabeth Tyler Kabara, Aaron Batista, and Byron MYu. Extracting low-dimensional latent structure from time series in the presence of delays. Neural computation, 27:1–32, 06 2015.

[19] Michael J Crawley. Analysis of variance: The R book. John Wiley & Sons, 2012.

[20] Ticiana L Coelho da Silva and et al. Big data analytics technologies and platforms:

A brief review. InLADaS@ VLDB, pages 25–32, 2018.

[21] Zhang Daoqiang and Zhou ZhiHua. (2d)2pca: Two-directional two-dimensional pca for efficient face representation and recognition.Neurocomputing, 69(1):224–231, 2005.

Neural Networks in Signal Processing.

[22] Jeffrey Dean and Sanjay Ghemawat. Mapreduce: simplified data processing on large clusters. Communications of the ACM, 51(1):107–113, 2008.

[23] Jens Dittrich, Ruiz Quian, and Arnulfo Jorge. Efficient big data processing in hadoop mapreduce. Proceedings of the VLDB Endowment, 5(12):2014–2015, 2012.

[24] Liang Du and Yi-Dong Shen. Unsupervised feature selection with adaptive struc-ture learning. InProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 209–218, New York, NY, USA, 2015.

ACM.

[25] Kokiopoulou E and Saad Y. Orthogonal neighborhood preserving projections: A projection-based dimensionality reduction technique. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12):2143–2156, 2007.

[26] Jaliya Ekanayake, Shrideep Pallickara, and Geoffrey Fox. Mapreduce for data inten-sive scientific analyses. In 2008 IEEE Fourth International Conference on eScience, pages 277–284. IEEE, 2008.

[27] Zhenyong Fu Elyor Kodirov, Tao Xiang and Shaogang Gong. Learning robust graph regularisation for subspace clustering. In Edwin R. Hancock Richard C. Wilson and William A. P. Smith, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 138.1–138.12. BMVA Press, September 2016.

[28] Ertam and Fatih. An effective gender recognition approach using voice data via deeper lstm networks. Applied Acoustics, 156:351–358, dec 2019.

[29] B Everitt and A Skrondal. The cambridge dictionary of statistics. Cambridge, Cam-bridge, 2010.

BIBLIOGRAPHY

[30] Brian Everitt and Anders Skrondal. Underfitting. The Cambridge Dictionary of Statistics, 409, 2010.

[31] Fan and K. On a theorem of weyl concerning eigenvalues of linear transformations:

Ii. Proceedings of the National Academy of Sciences of the United States of America, 36(1):31–35, jan 1950.

[32] Samaria Ferdinand and Harter Andy. Parameterisation of a stochastic model for human face identification. In WACV, 1994.

[33] Box George. Time Series Analysis: Forecasting & Control, 3/e. Pearson Education India, 1994.

[34] Daria Glushkova, Petar Jovanovic, and Alberto Abelló. Mapreduce performance model for hadoop 2. x. Information Systems, 79:32–43, 2017.

[35] Daria Glushkova, Petar Jovanovic, and Alberto Abelló. Mapreduce performance model for hadoop 2. x. Information Systems, 79:32–43, 2019.

[36] Jess Gonzalez and Wen Yu. Non-linear system modeling using LSTM neural networks.

IFAC-PapersOnLine, 51(13):485–489, jan 2018.

[37] Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber. Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10):2222–2232, 2017.

[38] John A Hartigan and Manchek A Wong. Algorithm as 136: A k-means cluster-ing algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1):100–108, 1979.

[39] Hawkins and Douglas M. The problem of overfitting.Journal of chemical information and computer sciences, 44(1):1–12, 2004.

[40] X He, S Yan, Y Hu, P Niyogi, and HJ Zhang. Face recognition using laplacianfaces.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(3):328–340, 2005.

[41] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural compu-tation, 9(8):1735–1780, 1997.

[42] Dewen Hu, Guiyu Feng, and Zongtan Zhou. Two-dimensional locality preserving projections (2dlpp) with its application to palmprint recognition. Pattern Recognition, 40:339–342, 04 2008.

[43] Graeme D Hutcheson. Ordinary least-squares regression. The SAGE dictionary of quantitative management research, pages 224–228, 2011.

[44] Son Huu and Hoang. Toward a proposed framework for mood recognition using LSTM Recurrent Neuron Network. Procedia Computer Science, 109:1028–1034, 2017.

[45] Joseph A Issa. Performance evaluation and estimation model using regression method for hadoop wordcount. IEEE Access, 3:2784–2793, 2015.

[46] F James. Monte carlo theory and practice.Reports on progress in Physics, 43(9):1145, 1980.

[47] Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.An introduction to statistical learning, volume 112. Springer, 2013.

[48] Shao Jun. Linear model selection by cross-validation. Journal of the American Sta-tistical Association, 88(422):486–494, 1993.

[49] Woraratpanya K, Sornnoi M, Leelaburanapong S, Titijaroonroj T, Varakulsiripunth R, Kurok Y, and Kato Y. An improved 2dpca for face recognition under illumination effects. In2015 7th International Conference on Information Technology and Electrical Engineering, pages 448–452, 2015.

[50] ShabnamN Kadir, DanFM Goodman, and KennethD Harris. High-dimensional clus-ter analysis with the masked em algorithm. Neural Computation, 26(11):2379–2394, 2014.

[51] Kambhatla, Nandakishore, and Todd Leen. Dimension reduction by local principal component analysis. Neural Comput, 9(7):1493–1516, 1997.

[52] Kamal Kc and Vincent W Freeh. Tuning hadoop map slot value using cpu metric.

In Workshop on Big Data Benchmarks, Performance Optimization, and Emerging Hardware, pages 141–153. Springer, 2014.

[53] W Kirch. Pearson’s correlation coefficient. Encyclopedia of Public Health; Springer:

Dordrecht, The Netherlands, pages 1090–2013, 2008.

[54] Jitendra Kumar, Rimsha Goomer, and Ashutosh Kumar Singh. Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting Model For Cloud Datacenters. Procedia Computer Science, 125:676–682, 2018.

[55] George Levy. Computational Finance Using C and C#: Derivatives and Valuation.

Academic Press, 2016.

[56] L.Gao, J.Song, F.Nie, Y.Yan, N.Sebe, and H.T.Shen. Optimal graph learning with partial tags and multiple features for image and video annotation. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4371–4379, 2015.

[57] Li, Lam Y, Van Do TBV, Chakka TV, and C R, Rotter. Investigation and character-ization of mapreduce applications for big data analytics.JOURNAL OF SCIENTIFIC and INDUSTRIAL RESEARCH, 2018.

[58] Yangyuan Li. Stable modeling on resource usage parameters of mapreduce applica-tion. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(2):45–62, 2018.

[59] Gao Lianli, Song Jingkuan, Liu Xingyi, Shao Junming, Liu Jiajun, and Shao Jie.

Learning in high-dimensional multimedia data: the state of the art. Multimedia Sys-tems, 23:303–313, 2015.

[60] Zelnik manor Lihi and Pietro Perona. Self-tuning spectral clustering. In Advances in Neural Information Processing Systems 17, pages 1601–1608. MIT Press, 2005.

[61] Yan Ling, Fang Liu, Yue Qiu, and Jiajie Zhao. Prediction of total execution time for mapreduce applications. In 2016 Sixth International Conference on Information Science and Technology (ICIST), pages 341–345. IEEE, 2016.

[62] Oliver Linton. Probability, statistics and econometrics. Academic Press, 2017.

BIBLIOGRAPHY

[63] Zheyuan Liu and Dejun Mu. Analysis of resource usage profile for mapreduce applica-tions using hadoop on cloud. In2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, pages 1500–1504. IEEE, 2012.

[64] Greta M Ljung and George EP Box. On a measure of lack of fit in time series models.

Biometrika, 65(2):297–303, 1978.

[65] Lyons M, Akamatsu S, Kamachi M, and Gyoba J. Coding facial expressions with gabor wavelets. In Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, pages 200–205, 1998.

[66] Turk M, A and Pentland A, P. Face recognition using eigenfaces. InProceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 586–591, 1991.

[67] Bhavin J Mathiya and Vinodkumar L Desai. Apache hadoop yarn mapreduce job classification based on cpu utilization and performance evaluation on multi-cluster heterogeneous environment. In Proceedings of International Conference on ICT for Sustainable Development, pages 35–44. Springer, 2016.

[68] Fan Mingyu, Chang Xiaojun, and Tao Dacheng. Structure regularized unsupervised discriminant feature analysis. In AAAI, 2017.

[69] Luo Minnan, Nie Feiping, Chang Xiaojun, Yang Yi, Alexander G.Hauptmann, and Qinghua Zheng. Avoiding optimal mean robust pca or 2dpca with non-greedy l1-norm maximization. InIJCAI, 2016.

[70] Mohar and Bojan. The laplacian spectrum of graphs. Graph theory, combinatorics, and applications., pages 871–898, 1991.

[71] Kumar Molugaram, G Shanker Rao, Anil Shah, and Naresh Davergave. Statistical techniques for transportation engineering. Butterworth-Heinemann, 2017.

[72] Shahzad Muzaffar and Afshin Afshari. Short-Term Load Forecasts Using LSTM Networks. Energy Procedia, 158:2922–2927, feb 2019.

[73] Laura L Nathans, Frederick L Oswald, and Kim Nimon. Interpreting multiple linear regression: A guidebook of variable importance. Practical Assessment, Research &

Evaluation, 17(9), 2012.

[74] SA Nene, SK Nayar, and H Murase. Columbia university image library (coil-20), 1996.

[75] Peter P Nghiem and Silvia M Figueira. Towards efficient resource provisioning in mapreduce. Journal of Parallel and Distributed Computing, 95:29–41, 2016.

[76] Feiping Nie, Xiaoqian Wang, and Heng Huang. Clustering and projected clustering with adaptive neighbors. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 977–986, New York, NY, USA, 2014. ACM.

[77] Feiping Nie, Jianjun Yuan, and Heng Huang. Optimal mean robust principal com-ponent analysis. In Proceedings of the 31st International Conference on Machine Learning, pages 1062–1070. JMLR.org, 2014.

[78] O�brien and M Robert. A caution regarding rules of thumb for variance inflation factors. ”Quality & quantity”, 41(5):673–690, 2007.

[79] Pal and Ranadip. Predictive modeling of drug sensitivity. Academic Press, 2016.

[80] Piela Peter. Collectl, 2017.

[81] Phillips P.Jonathon, Wechsler Harry, and J. Rauss Jeffery, Huangand Patrick. The feret database and evaluation procedure for face-recognition algorithms. Image and Vision Computing, 16(5):295–306, 1998.

[82] Nils Reimers and Iryna Gurevych. Optimal hyperparameters for deep lstm-networks for sequence labeling tasks. arXiv preprint arXiv:1707.06799, 2017.

[83] R Reka, K Saraswathi, and K Sujatha. A review on big data analytics. Asian Journal of Applied Science and Technology (AJAST), 1(1):233–234, 2017.

[84] N Babaii Rizvandi, Abdolreza Nabavi, and SH HESSABI. An accurate fir approxima-tion of ideal fracapproxima-tional delay filter with complex coefficients in hilbert space. Journal of Circuits, Systems, and Computers, 14(03):497–505, 2005.

[85] Nikzad Babaii Rizvandi, Javid Taheri, Reza Moraveji, and Albert Y. Zomaya. On modelling and prediction of total cpu usage for applications in mapreduce environ-ments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012.

[86] David P Rodgers. Improvements in multiprocessor system design. ACM SIGARCH Computer Architecture News, 13(3):225–231, 1985.

[87] Sheldon M Ross. Introductory statistics. Academic Press, 2017.

[88] Sherif Sakr. Big data 2.0 processing systems: a survey. Springer, 2016.

[89] Afan Galih Salman, Yaya Heryadi, Edi Abdurahman, and Wayan Suparta. Sin-gle Layer & Multi-layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting. Procedia Computer Science, 135:89–98, jan 2018.

[90] Mohamed Bin Shams, Shaker Haji, Ali Salman, Hussain Abdali, and Alaa Alsaffar.

Time series analysis of bahrain’s first hybrid renewable energy system. Energy, 103:1–

15, 2016.

[91] Yuliang Shi, Kaihui Zhang, Lizhen Cui, Lei Liu, Yongqing Zheng, Shidong Zhang, and Han Yu. Mapreduce short jobs optimization based on resource reuse. Microprocessors and Microsystems, 47:178–187, 2016.

[92] Konstantin Shvachko, Hairong Kuang, Sanjay Radia, Robert Chansler, et al. The hadoop distributed file system. In MSST, volume 10, pages 1–10, 2010.

[93] Jasper Snoek, Hugo Larochelle, and Ryan P Adams. Practical bayesian optimization of machine learning algorithms. InAdvances in neural information processing systems, pages 2951–2959, 2012.

[94] Ge Song, Zide Meng, Fabrice Huet, Frederic Magoules, Lei Yu, and Xuelian Lin. A hadoop mapreduce performance prediction method. In 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE In-ternational Conference on Embedded and Ubiquitous Computing, pages 820–825. IEEE, 2013.

BIBLIOGRAPHY

[95] Jingkuan Song, Lianli Gao, Mihai Marian Puscas, Feiping Nie, Fumin Shen, and Nicu Sebe. Joint graph learning and video segmentation via multiple cues and topology calibration. InProceedings of the 24th ACM International Conference on Multimedia, pages 831–840, New York, NY, USA, 2016. ACM.

[96] Shortreed Susan and Meila Marina. Unsupervised spectral learning. In Proceed-ings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05), pages 534–541, Arlington, Virginia, 2005. AUAI Press.

[97] Shlomo Ta asan, G Kuruvila, and M Salas. Aerodynamic design and optimization in one shot. In 30th aerospace sciences meeting and exhibit, page 25, 1992.

[98] Sergios Theodoridis. Machine learning: a Bayesian and optimization perspective.

Academic Press, 2015.

[99] Yan Tian, Kaili Zhang, Jianyuan Li, Xianxuan Lin, and Bailin Yang. LSTM-based traffic flow prediction with missing data. Neurocomputing, 318:297–305, 2018.

[100] Hastie Trevor, Tibshirani Robert, and Friedman JH. The elements of statistical learning: data mining, inference, and prediction, 2009.

[101] Sam TRoweis and Lawrence K Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290:2323–2326, 01 2001.

[102] Joseph Usset, Ana-Maria Staicu, and Arnab Maity. Interaction models for functional regression. Computational statistics & data analysis, 94:317–329, 2016.

[103] Vinod Kumar Vavilapalli and et al. Apache hadoop yarn: Yet another resource negotiator. InProceedings of the 4th annual Symposium on Cloud Computing, page 5.

ACM, 2013.

[104] Vinod Kumar Vavilapalli and et al. Apache hadoop yarn: Yet another resource negotiator. InProceedings of the 4th annual Symposium on Cloud Computing, page 5.

ACM, 2013.

[105] Luis Eduardo Bautista Villalpando, Alain April, and Alain Abran. Performance analysis model for big data applications in cloud computing. Journal of Cloud Com-puting, 3(1):19, 2014.

[106] Jin Wang, Bo Peng, and Xuejie Zhang. Using a stacked residual LSTM model for sentiment intensity prediction. Neurocomputing, 322:93–101, dec 2018.

[107] Kewen Wang, Xuelian Lin, and Wenzhong Tang. Predator—an experience guided configuration optimizer for hadoop mapreduce. In4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, pages 419–426. IEEE, 2012.

[108] Xiaoqian Wang, Yun Liu, Feiping Nie, and Heng Huang. Discriminative unsuper-vised dimensionality reduction. In Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.

[109] Robert Warner. Optimizing the display and interpretation of data. Elsevier, 2015.

[110] Tom White. Hadoop: The definitive guide. O’Reilly Media, Inc., 2012.

[111] Ian.H Witten, Eibe Frank, Mark.A Hall, and Christopher.J Pal. Data Mining:

Practical machine learning tools and techniques. Morgan Kaufmann, 2017.

[112] Svante Wold, Arnold Ruhe, Herman Wold, and WJ Dunn, III. The collinearity problem in linear regression. the partial least squares approach to generalized inverses.

SIAM Journal on Scientific and Statistical Computing, 5(3):735–743, 1984.

[113] Mingrui Wu and Bernhard Scholkopf. A local learning approach for clustering.

In Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, 1529-1536 (2007), pages 1529–1536, 01 2006.

[114] Chang X and Yang Y. Semisupervised feature analysis by mining correlations among multiple tasks. IEEE Transactions on Neural Networks and Learning Systems, 28(10):2294–2305, 2017.

[115] Chang X, Yu Y, Yang Y, and Xing EP. Semantic pooling for complex event analysis in untrimmed videos. IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 39(8):1617–1632, 2017.

[116] He X, Ji M, Zhang C, and Bao H. A variance minimization criterion to feature selection using laplacian regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10):2013–2025, 2011.

[117] Jiang X and Mojon D. Adaptive local thresholding by verification-based multithresh-old probing with application to vessel detection in retinal images. IEEE Transactions on Pattern Analysis Machine Intelligence, 26(01):131–137, 2003.

[118] He Xiaofei and Niyogi Partha. Locality preserving projections. In In Advances in Neural Information Processing Systems 16. MIT Press, 2003.

[119] Chang Xiaojun, Ma Zhigang, Lin Ming, Yang Yi, G Alexander, and Hauptmann.

Feature interaction augmented sparse learning for fast kinect motion detection. IEEE Transactions on Image Processing, 26:3911–3920, 2017.

[120] Dong Xiaoqian, Huang Huan, and Wen Hongyan. A comparative study of several face recognition algorithms based on pca. In The Third International Symposium Computer Science and Computational Technology (ISCSCT 2010), page 443, 2010.

[121] Yang Y, Xu D, Nie F, Yan S, and Zhuang Y. Image clustering using local dis-criminant models and global integration. IEEE Transactions on Image Processing, 19(10):2761–2773, 2010.

[122] Bailin Yang, Shulin Sun, Jianyuan Li, Xianxuan Lin, and Yan Tian. Traffic flow pre-diction using LSTM with feature enhancement. Neurocomputing, 332:320–327, 2019.

[123] Hailong Yang, Zhongzhi Luan, Wenjun Li, and Depei Qian. Mapreduce workload modeling with statistical approach. Journal of grid computing, 10(2):279–310, 2012.

[124] Xin Yue Yang, Zhen Liu, and Yan Fu. Mapreduce as a programming model for asso-ciation rules algorithm on hadoop. InThe 3rd International Conference on Information Sciences and Interaction Sciences, pages 99–102. IEEE, 2010.

[125] Yi Yang, HengTao Shen, Feiping Nie, Rongrong Ji, and Xiaofang Zhou. Nonnegative spectral clustering with discriminative regularization. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, AAAI’11, pages 555–560. AAAI Press, 2011.

[126] Li YiFei and Han Cao. Prediction for tourism flow based on lstm neural network.

Procedia Computer Science, 129:277–283, 2018.