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Artículos de revistas sobre el tema "Dimensionality reduction"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

Cheng, Long, Chenyu You, and Yani Guan. "Random Projections for Non-linear Dimensionality Reduction." International Journal of Machine Learning and Computing 6, no. 4 (2016): 220–25. http://dx.doi.org/10.18178/ijmlc.2016.6.4.601.

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2

Marchette, David J., and Wendy L. Poston. "Local dimensionality reduction." Computational Statistics 14, no. 4 (1999): 469–89. http://dx.doi.org/10.1007/s001800050026.

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3

Sun, Yu-Yin, Michael Ng, and Zhi-Hua Zhou. "Multi-Instance Dimensionality Reduction." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 587–92. http://dx.doi.org/10.1609/aaai.v24i1.7700.

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Multi-instance learning deals with problems that treat bags of instances as training examples. In single-instance learning problems, dimensionality reduction is an essential step for high-dimensional data analysis and has been studied for years. The curse of dimensionality also exists in multiinstance learning tasks, yet this difficult task has not been studied before. Direct application of existing single-instance dimensionality reduction objectives to multi-instance learning tasks may not work well since it ignores the characteristic of multi-instance learning that the labels of bags are kno

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4

Koren, Y., and L. Carmel. "Robust linear dimensionality reduction." IEEE Transactions on Visualization and Computer Graphics 10, no. 4 (2004): 459–70. http://dx.doi.org/10.1109/tvcg.2004.17.

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5

Lotlikar, R., and R. Kothari. "Fractional-step dimensionality reduction." IEEE Transactions on Pattern Analysis and Machine Intelligence 22, no. 6 (2000): 623–27. http://dx.doi.org/10.1109/34.862200.

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6

Gottlieb, Lee-Ad, Aryeh Kontorovich, and Robert Krauthgamer. "Adaptive metric dimensionality reduction." Theoretical Computer Science 620 (March 2016): 105–18. http://dx.doi.org/10.1016/j.tcs.2015.10.040.

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7

Pang, Rich, Benjamin J. Lansdell, and Adrienne L. Fairhall. "Dimensionality reduction in neuroscience." Current Biology 26, no. 14 (2016): R656—R660. http://dx.doi.org/10.1016/j.cub.2016.05.029.

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8

Lovaglio, Pietro Giorgio, and Giorgio Vittadini. "Multilevel dimensionality-reduction methods." Statistical Methods & Applications 22, no. 2 (2012): 183–207. http://dx.doi.org/10.1007/s10260-012-0215-2.

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9

Carter, Kevin, Raviv Raich, William Finn, and Alfred Hero,III. "Information-Geometric Dimensionality Reduction." IEEE Signal Processing Magazine 28, no. 2 (2011): 89–99. http://dx.doi.org/10.1109/msp.2010.939536.

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10

Gonen, Mehmet. "Bayesian Supervised Dimensionality Reduction." IEEE Transactions on Cybernetics 43, no. 6 (2013): 2179–89. http://dx.doi.org/10.1109/tcyb.2013.2245321.

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11

Zhang, Zhao, Tommy W. S. Chow, and Ning Ye. "SEMISUPERVISED MULTIMODAL DIMENSIONALITY REDUCTION." Computational Intelligence 29, no. 1 (2012): 70–110. http://dx.doi.org/10.1111/j.1467-8640.2012.00429.x.

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12

Liu, Huan, and Rudy Setiono. "Dimensionality reduction via discretization." Knowledge-Based Systems 9, no. 1 (1996): 67–72. http://dx.doi.org/10.1016/0950-7051(95)01030-0.

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13

Yang, Li. "Distance-preserving dimensionality reduction." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1, no. 5 (2011): 369–80. http://dx.doi.org/10.1002/widm.39.

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14

Li, Hongda, Jian Cui, Xinle Zhang, Yongqi Han, and Liying Cao. "Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction." Remote Sensing 14, no. 18 (2022): 4579. http://dx.doi.org/10.3390/rs14184579.

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Terrain classification is an important research direction in the field of remote sensing. Hyperspectral remote sensing image data contain a large amount of rich ground object information. However, such data have the characteristics of high spatial dimensions of features, strong data correlation, high data redundancy, and long operation time, which lead to difficulty in image data classification. A data dimensionality reduction algorithm can transform the data into low-dimensional data with strong features and then classify the dimensionally reduced data. However, most classification methods ca

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15

Ahmad, Noor, and Ali Bou Nassif. "Dimensionality Reduction: Challenges and Solutions." ITM Web of Conferences 43 (2022): 01017. http://dx.doi.org/10.1051/itmconf/20224301017.

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The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dimensional data. These techniques gather several data features of interest, such as dynamical structure, input-output relationships, the correlation between data sets, covariance, etc. Dimensionality reduction entails mapping a set of high dimensional data features onto low dimensional data. Motivated by the lack of learning models’ performance due to the high dimensionality data, this study encounters five distinct dimensionality reduction methods. Besides, a comparison between reduced dimensiona

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16

Shen, Zilin. "Comparison and Evaluation of Classical Dimensionality Reduction Methods." Highlights in Science, Engineering and Technology 70 (November 15, 2023): 411–18. http://dx.doi.org/10.54097/hset.v70i.13890.

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As one of the tasks of unsupervised learning, data dimensionality reduction is faced with the problem of a lack of evaluation methods. Based on this, three classical dimensionality reduction methods such as PCA, t-SNE and UMAP were selected as the research object in this paper. This article selected 5 three-classification datasets and used the three methods mentioned above to perform dimensionality reduction. This paper plotted 3D scatter graphs after dimensionality reduction to analyze the differentiation effect of the data on different categories of the target variable. Then the data after d

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17

Tiwari, Anamika. "Classification of Emotion from Facial Image Using Dimensionality Reduction Technique." International Journal of Scientific Engineering and Research 4, no. 3 (2016): 11–13. https://doi.org/10.70729/29031601.

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18

K, Bhargavi. "Data Dimensionality Reduction Techniques : Review." International Journal of Engineering Technology and Management Sciences 4, no. 4 (2020): 62–65. http://dx.doi.org/10.46647/ijetms.2020.v04i04.010.

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Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured. Data science is related to computer science, but is a separate field. Computer science involves creating programs and algorithms to record and process data, while data science covers any type of data analysis, which may or may not use computers. Data science is more closely related to the mathematics field of Statistics, which inc

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19

Kay, Steven. "Dimensionality Reduction for Signal Detection." IEEE Signal Processing Letters 29 (2022): 145–48. http://dx.doi.org/10.1109/lsp.2021.3129453.

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20

Nelson, Jelani. "Dimensionality Reduction in Euclidean Space." Notices of the American Mathematical Society 67, no. 10 (2020): 1. http://dx.doi.org/10.1090/noti2166.

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21

Tianhao Zhang, Dacheng Tao, Xuelong Li, and Jie Yang. "Patch Alignment for Dimensionality Reduction." IEEE Transactions on Knowledge and Data Engineering 21, no. 9 (2009): 1299–313. http://dx.doi.org/10.1109/tkde.2008.212.

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22

Wang, Shujian, Deyan Xie, Fang Chen, and Quanxue Gao. "Dimensionality reduction by LPP‐L21." IET Computer Vision 12, no. 5 (2018): 659–65. http://dx.doi.org/10.1049/iet-cvi.2017.0302.

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23

Raymer, M. L., W. F. Punch, E. D. Goodman, L. A. Kuhn, and A. K. Jain. "Dimensionality reduction using genetic algorithms." IEEE Transactions on Evolutionary Computation 4, no. 2 (2000): 164–71. http://dx.doi.org/10.1109/4235.850656.

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24

Saund, E. "Dimensionality-reduction using connectionist networks." IEEE Transactions on Pattern Analysis and Machine Intelligence 11, no. 3 (1989): 304–14. http://dx.doi.org/10.1109/34.21799.

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25

Vats, Deepak, and Avinash Sharma. "Dimensionality Reduction Techniques: Comparative Analysis." Journal of Computational and Theoretical Nanoscience 17, no. 6 (2020): 2684–88. http://dx.doi.org/10.1166/jctn.2020.8967.

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It has been spotted an exponential growth in terms of dimension in real world data. Some example of higher dimensional data may includes speech signal, sensor data, medical data, criminal data and data related to recommendation process for different field like news, movies (Netflix) and e-commerce. To empowering learning accuracy in the area of machine learning and enhancing mining performance one need to remove redundant feature and feature not relevant for mining and learning task from this high dimension dataset. There exist many supervised and unsupervised methodologies in literature to pe

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26

Harrow, Aram W., Ashley Montanaro, and Anthony J. Short. "Limitations on quantum dimensionality reduction." International Journal of Quantum Information 13, no. 04 (2015): 1440001. http://dx.doi.org/10.1142/s0219749914400012.

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The Johnson–Lindenstrauss Lemma is a classic result which implies that any set of n real vectors can be compressed to O( log n) dimensions while only distorting pairwise Euclidean distances by a constant factor. Here we consider potential extensions of this result to the compression of quantum states. We show that, by contrast with the classical case, there does not exist any distribution over quantum channels that significantly reduces the dimension of quantum states while preserving the 2-norm distance with high probability. We discuss two tasks for which the 2-norm distance is indeed the co

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27

Tasoulis, Sotiris, Nicos G. Pavlidis, and Teemu Roos. "Nonlinear dimensionality reduction for clustering." Pattern Recognition 107 (November 2020): 107508. http://dx.doi.org/10.1016/j.patcog.2020.107508.

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28

Gao, Junbin, Qinfeng Shi, and Tibério S. Caetano. "Dimensionality reduction via compressive sensing." Pattern Recognition Letters 33, no. 9 (2012): 1163–70. http://dx.doi.org/10.1016/j.patrec.2012.02.007.

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29

Wang, Yasi, Hongxun Yao, and Sicheng Zhao. "Auto-encoder based dimensionality reduction." Neurocomputing 184 (April 2016): 232–42. http://dx.doi.org/10.1016/j.neucom.2015.08.104.

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30

Hou, Chenping, Changshui Zhang, Yi Wu, and Yuanyuan Jiao. "Stable local dimensionality reduction approaches." Pattern Recognition 42, no. 9 (2009): 2054–66. http://dx.doi.org/10.1016/j.patcog.2008.12.009.

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31

Qiao, Lishan, Limei Zhang, and Songcan Chen. "Dimensionality reduction with adaptive graph." Frontiers of Computer Science 7, no. 5 (2013): 745–53. http://dx.doi.org/10.1007/s11704-013-2234-z.

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32

Lai, Zhihui, Yong Xu, Jian Yang, Linlin Shen, and David Zhang. "Rotational Invariant Dimensionality Reduction Algorithms." IEEE Transactions on Cybernetics 47, no. 11 (2017): 3733–46. http://dx.doi.org/10.1109/tcyb.2016.2578642.

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33

Nagabhushan, P., K. Chidananda Gowda, and Edwin Diday. "Dimensionality reduction of symbolic data." Pattern Recognition Letters 16, no. 2 (1995): 219–23. http://dx.doi.org/10.1016/0167-8655(94)00085-h.

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34

Havelka, Jan, Anna Kučerová, and Jan Sýkora. "Dimensionality reduction in thermal tomography." Computers & Mathematics with Applications 78, no. 9 (2019): 3077–89. http://dx.doi.org/10.1016/j.camwa.2019.04.019.

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35

Santos, João Filipe, Maria Manuela Portela, and Inmaculada Pulido-Calvo. "Dimensionality reduction in drought modelling." Hydrological Processes 27, no. 10 (2012): 1399–410. http://dx.doi.org/10.1002/hyp.9300.

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36

Xie, Fuding, Yutao Fan, and Ming Zhou. "Dimensionality Reduction by Weighted Connections between Neighborhoods." Abstract and Applied Analysis 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/928136.

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Dimensionality reduction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality. This paper introduces a dimensionality reduction technique by weighted connections between neighborhoods to improveK-Isomap method, attempting to preserve perfectly the relationships between neighborhoods in the process of dimensionality reduction. The validity of the proposal is tested by three typical examples which are widely employed in the algorithms based on manifold. The experimental results show that the local topology nature of dataset is preserved well w

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37

Md., Abu Marjan, Rashedul Islam Md., Palash Uddin Md., Ibn Afjal Masud, and Al Mamun Md. "PCA-based dimensionality reduction for face recognition." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 5 (2021): 1622–29. https://doi.org/10.12928/telkomnika.v19i5.19566.

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In this paper, we conduct a comprehensive study on dimensionality reduction (DR) techniques and discuss the mostly used statistical DR technique called principal component analysis (PCA) in detail with a view to addressing the classical face recognition problem. Therefore, we, more devotedly, propose a solution to either a typical face or individual face recognition based on the principal components, which are constructed using PCA on the face images. We simulate the proposed solution with several training and test sets of manually captured face images and also with the popular Olivetti Resear

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38

Zhao, Xiaowei, Feiping Nie, Sen Wang, Jun Guo, Pengfei Xu, and Xiaojiang Chen. "Unsupervised 2D Dimensionality Reduction with Adaptive Structure Learning." Neural Computation 29, no. 5 (2017): 1352–74. http://dx.doi.org/10.1162/neco_a_00950.

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In recent years, unsupervised two-dimensional (2D) dimensionality reduction methods for unlabeled large-scale data have made progress. However, performance of these degrades when the learning of similarity matrix is at the beginning of the dimensionality reduction process. A similarity matrix is used to reveal the underlying geometry structure of data in unsupervised dimensionality reduction methods. Because of noise data, it is difficult to learn the optimal similarity matrix. In this letter, we propose a new dimensionality reduction model for 2D image matrices: unsupervised 2D dimensionality

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39

Chun-Man Yan, Chun-Man Yan, and Yu-Yao Zhang Chun-Man Yan. "Face Recognition Based on SRC Combined with Sparse Embedding Dimensionality Reduction." 電腦學刊 33, no. 2 (2022): 083–93. http://dx.doi.org/10.53106/199115992022043302007.

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<p>Sparse representation-based classification (SRC) method has achieved good recognition results and shown strong robustness for face recognition, especially when the face image is affected by illumination variations, expression changes and occlusion. SRC method simply uses the training set as a dictionary to encode test samples. However, the high-dimensional training face data usually contain a large amount of redundant information, which will increase the complexity of this method. Therefore, the image dimensionality reduction procedure is separately performed by most of the existing m

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40

Chun-Man Yan, Chun-Man Yan, and Yu-Yao Zhang Chun-Man Yan. "Face Recognition Based on SRC Combined with Sparse Embedding Dimensionality Reduction." 電腦學刊 33, no. 2 (2022): 083–93. http://dx.doi.org/10.53106/199115992022043302007.

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<p>Sparse representation-based classification (SRC) method has achieved good recognition results and shown strong robustness for face recognition, especially when the face image is affected by illumination variations, expression changes and occlusion. SRC method simply uses the training set as a dictionary to encode test samples. However, the high-dimensional training face data usually contain a large amount of redundant information, which will increase the complexity of this method. Therefore, the image dimensionality reduction procedure is separately performed by most of the existing m

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41

Vijayarani, Dr S., Dr C. Sivamathi, and Mrs S. Maria Sylviaa. "Bio Inspired Algorithms for Dimensionality Reduction and Outlier Detection in Medical Datasets." International Journal of Advanced Networking and Applications 14, no. 01 (2022): 5277–86. http://dx.doi.org/10.35444/ijana.2022.14107.

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Dimensionality Reduction is one of the useful techniques used in number of applications in order to reduce the number of features to improve the productivity and efficiency of the task. Clustering is one of the influential tasks in data mining. Dimensionality reductions are used in data mining, Image processing, Networking, Mobile computing, etc. The elementary intention of this work is to apply dimensionality reduction algorithms and then cluster the datasets to detect outliers. A bio-inspired ACO (Ant Colony optimization) algorithm has been proposed to reduce dimensionality. Also another bio

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42

Remesh, Reshma, and Pattabiraman V. "A SURVEY ON THE CURES FOR THE CURSE OF DIMENSIONALITY IN BIG DATA." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 355. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19755.

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Dimensionality reduction techniques are used to reduce the complexity for analysis of high dimensional data sets. The raw input data set may have large dimensions and it might consume time and lead to wrong predictions if unnecessary data attributes are been considered for analysis. So using dimensionality reduction techniques one can reduce the dimensions of input data towards accurate prediction with less cost. In this paper the different machine learning approaches used for dimensionality reductions such as PCA, SVD, LDA, Kernel Principal Component Analysis and Artificial Neural Network hav

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43

Gao, Yunlong, Sizhe Luo, Jinyan Pan, Zhihao Wang, and Peng Gao. "Kernel alignment unsupervised discriminative dimensionality reduction." Neurocomputing 453 (September 2021): 181–94. http://dx.doi.org/10.1016/j.neucom.2021.03.127.

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44

Crespo, Luis G., Brendon K. Colbert, Sean P. Kenny, and Daniel P. Giesy. "Dimensionality Reduction of Sliced-Normal Distributions." IFAC-PapersOnLine 53, no. 2 (2020): 7412–17. http://dx.doi.org/10.1016/j.ifacol.2020.12.1275.

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45

Petty, G. W. "Dimensionality reduction in Bayesian estimation algorithms." Atmospheric Measurement Techniques 6, no. 9 (2013): 2267–76. http://dx.doi.org/10.5194/amt-6-2267-2013.

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Abstract. An idealized synthetic database loosely resembling 3-channel passive microwave observations of precipitation against a variable background is employed to examine the performance of a conventional Bayesian retrieval algorithm. For this dataset, algorithm performance is found to be poor owing to an irreconcilable conflict between the need to find matches in the dependent database versus the need to exclude inappropriate matches. It is argued that the likelihood of such conflicts increases sharply with the dimensionality of the observation space of real satellite sensors, which may util

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46

Petty, G. W. "Dimensionality reduction in Bayesian estimation algorithms." Atmospheric Measurement Techniques Discussions 6, no. 2 (2013): 2327–52. http://dx.doi.org/10.5194/amtd-6-2327-2013.

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Abstract. An idealized synthetic database loosely resembling 3-channel passive microwave observations of precipitation against a variable background is employed to examine the performance of a conventional Bayesian retrieval algorithm. For this dataset, algorithm performance is found to be poor owing to an irreconcilable conflict between the need to find matches in the dependent database versus the need to exclude inappropriate matches. It is argued that the likelihood of such conflicts increases sharply with the dimensionality of the observation space of real satellite sensors, which may util

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47

Mahadev, Preeti, and P. Nagabhushan. "Incremental Dimensionality Reduction in Hyperspectral Data." International Journal of Computer Applications 163, no. 7 (2017): 21–34. http://dx.doi.org/10.5120/ijca2017913575.

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48

Jindal, Priyanka, and Dharmender Kumar. "A Review on Dimensionality Reduction Techniques." International Journal of Computer Applications 173, no. 2 (2017): 42–46. http://dx.doi.org/10.5120/ijca2017915260.

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49

Merola, Giovanni M., and Bovas Abraham. "Dimensionality reduction approach to multivariate prediction." Canadian Journal of Statistics 29, no. 2 (2001): 191–200. http://dx.doi.org/10.2307/3316072.

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50

Zhikai Zhao, Jiansheng Qian, and Jian Cheng. "Marginal Discriminant Projection for Dimensionality Reduction." International Journal of Digital Content Technology and its Applications 6, no. 15 (2012): 1–11. http://dx.doi.org/10.4156/jdcta.vol6.issue15.1.

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