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A SURVEY OF REMOTE SENSING IMAGE CLASSIFICATION APPROACHES
English

2015 International Journal of Applied Mathematics and Machine Learning  
Remote-sensing image classification is a complex process that may be affected via lots of factors.  ...  This paper review suggests that designing a suitable image processing procedure is a prerequisite for a successful classification of remotely sensed data and the selection of a suitable classification  ...  In summary, the error matrix approach is the most common accuracy assessment approach for categorical classes.  ... 
doi:10.18642/ijamml_7100121516 fatcat:xci6yaus35d6dmsiaklrort2me

Enter the Matrix: Factorization Uncovers Knowledge from Omics

Genevieve L. Stein-O'Brien, Raman Arora, Aedin C. Culhane, Alexander V. Favorov, Lana X. Garmire, Casey S. Greene, Loyal A. Goff, Yifeng Li, Aloune Ngom, Michael F. Ochs, Yanxun Xu, Elana J. Fertig
2018 Trends in Genetics  
Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions.  ...  These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis.  ...  Acknowledgments Supplemental Information Supplemental information associated with this article can be found, in the online version, at https://doi.org/10.1016/j.tig. 2018.07.003.  ... 
doi:10.1016/j.tig.2018.07.003 pmid:30143323 pmcid:PMC6309559 fatcat:t75nfkw5sjcs3lbhjt5aejrm3i

Digital Intuition: Applying Common Sense Using Dimensionality Reduction

Catherine Havasi, Robyn Speer, James Pustejovsky, Henry Lieberman
2009 IEEE Intelligent Systems  
Applying SVD to Common Sense To use this technique to make inferences about common sense, we express ConceptNet as a matrix.  ...  This generalizes a method of commonsense inference called cumulative analogy, first presented in Timothy Chklovski's Learner. 6 Using SVD, any matrix A can be factored into an orthonormal matrix U, a  ... 
doi:10.1109/mis.2009.84 fatcat:kar7re4p7fg4lgypxu44zxta7y

Digital Intuition: Applying Common Sense Using Dimensionality Reduction

Catherine Havasi, Robyn Speer, James Pustejovsky, Henry Lieberman
2010 IEEE Intelligent Systems  
Applying SVD to Common Sense To use this technique to make inferences about common sense, we express ConceptNet as a matrix.  ...  This generalizes a method of commonsense inference called cumulative analogy, first presented in Timothy Chklovski's Learner. 6 Using SVD, any matrix A can be factored into an orthonormal matrix U, a  ... 
doi:10.1109/mis.2010.4 fatcat:qcmgvubc5jabpkzura7rdnhmi4

Analytical framework for optimized feature extraction for upgrading occupancy sensing performance

Preethi Krishna Rao Mane, K. Narasimha Rao
2020 International Journal of Electrical and Computer Engineering (IJECE)  
Therefore, this paper introduces a simplified framework for enriching the event sensing performance by efficient selection of minimal robust attributes using Doppler radar.  ...  Adoption of analytical methodology has been carried out to find that different machine learning approaches could be further used for improving the accuracy performance for the feature that has been extracted  ...  The specific forms of sensors are used in the work of Liu et al. [22] for occupancy sensing by using the hidden markov model. The idea of environmental factor sharing is discussed in Jin et al.  ... 
doi:10.11591/ijece.v10i4.pp4093-4100 fatcat:4fwlswu3jvhy5mbgbtotgct5tq

Digital Intuition: Applying Common Sense Using Dimensionality Reduction

Catherine Havasi, Robyn Speer, James Pustejovsky, Henry Lieberman
2009 IEEE Intelligent Systems  
Applying SVD to Common Sense To use this technique to make inferences about common sense, we express ConceptNet as a matrix.  ...  This generalizes a method of commonsense inference called cumulative analogy, first presented in Timothy Chklovski's Learner. 6 Using SVD, any matrix A can be factored into an orthonormal matrix U, a  ... 
doi:10.1109/mis.2009.72 fatcat:xxfhcaocwrhlxpoce33u62jyeu

The assessment of similarity vectors of fingerprint and UMLS in adverse drug reaction prediction [article]

Milad Besharatifard, Zahra Ghorbanali, Fatemeh Zare-Mirakabad
2021 bioRxiv   pre-print
However, machine learning frameworks with fewer drug and adverse reaction features are more accurate than matrix factorization frameworks.  ...  The rest of them improve two state-of-art matrix factorization models, CS and TMF, by considering target as a drug feature and phenotype as an adverse reaction feature.  ...  [15] used the compressed sensing (CS) model as a matrix factorization to predict unknown relationships between drugs and adverse reactions.  ... 
doi:10.1101/2021.11.15.468509 fatcat:fka4cwwf5fhetb7eu2ncxmj2r4

Gaussian Process Multi-task Learning Using Joint Feature Selection [chapter]

P. K. Srijith, Shirish Shevade
2014 Lecture Notes in Computer Science  
In this paper, we propose a novel Gaussian Process (GP) approach to multi-task learning based on joint feature selection.  ...  A promising idea to multi-task learning is joint feature selection where a sparsity pattern is shared across task specific feature representations.  ...  In Bayesian multi-task feature selection [11] , a spike and slab prior is used to enforce the selection of a common subset of features across the tasks.  ... 
doi:10.1007/978-3-662-44845-8_7 fatcat:tqtuamjfs5dmxbifggzaqoejje

MIL: Automatic Metaphor Identification by Statistical Learning

Yosef Ben Shlomo, Mark Last
2015 European Conference on Principles of Data Mining and Knowledge Discovery  
In this paper, we present a new, supervised learning approach called MIL (Metaphor Identification by Learning), for identifying three major types of metaphoric expressions without using any knowledge resources  ...  Metaphor identification in text is an open problem in natural language processing.  ...  This can be done using existing natural language processing tools. We also use the domain corpus to construct a word-context matrix to be used in the feature extraction phase.  ... 
dblp:conf/pkdd/ShlomoL15 fatcat:qpshi6z6gjcivialuisgrux7he

Classification for Remote Sensing Data with Improved CNN-SVM Method

Xiankun Sun, Lan Liu, Chengfan Li, Jingyuan Yin, Junjuan Zhao, Wen Si
2019 IEEE Access  
Next, taking the simulation experiments of MNIST handwritten digital dataset and UC Merced Land Use remote sensing dataset as examples, we tested and verified the proposed method in this experiment.  ...  We then inputted the RSI into the CNN model and extracted feature maps and replaced the output layer of the CNN network via training the feature maps in the SVM classifier.  ...  Via selecting a particular way, the pooling layer can compress the feature map learned by the convolutional layer.  ... 
doi:10.1109/access.2019.2952946 fatcat:jkcjqhd7ljalhbtr2alrtc6pku

Nonlinear Traffic Data Reconstruction in large-scale Internet of Vehicle Systems: A neural network approach

Zhenquan Qin, Jian Fang, Bingxian Lu, Xu Xia, Lei Wang, Shan Gao
2022 IEEE Access  
NRTD instantiates the process of generalized matrix factorization and combines the potential features of multilayer perceptrons to improve the fitting degree of the model to the traffic flow data change  ...  INDEX TERMS Multi-access edge computing, traffic data reconstruction, neural networks, matrix factorization.  ...  The reason is that the method of using matrix decomposition to reconstruct data in a common compressed sensing reconstruction model has limitations.  ... 
doi:10.1109/access.2022.3160464 fatcat:tklzxjvhdfd3dmlupzrm4n442m

Understanding the Spatio-temporal Topic Dynamics of Covid-19 using Nonnegative Tensor Factorization: A Case Study [article]

Thirunavukarasu Balasubramaniam, Richi Nayak, Md Abul Bashar
2020 arXiv   pre-print
This paper proposes a tensor-based representation of social media data and Non-negative Tensor Factorization (NTF) to identify the topics discussed in social media data along with the spatio-temporal topic  ...  This huge collection of data, if analysed properly, can provide useful insights into people's behavior.  ...  In the same example, the selected column from the temporal factor matrix T has the high weights towards the end.  ... 
arXiv:2009.09253v1 fatcat:xhm5p3q7y5aj5blwhiuof2qb6m

Understanding the Spatio-temporal Topic Dynamics of Covid-19 using Nonnegative Tensor Factorization: A Case Study

Thirunavukarasu Balasubramaniam, Richi Nayak, Md Abul Bashar
2020 2020 IEEE Symposium Series on Computational Intelligence (SSCI)  
In the same example, the selected column from the temporal factor matrix T has the high weights towards the end.  ...  For example, the spatial patterns learned from Fig. 1a can help to devise the collection of common terms used in specific locations, however, it fails to learn the associations such as how the collection  ... 
doi:10.1109/ssci47803.2020.9308265 fatcat:lgwa2hn4jja23iv3c7th7goinm

A Novel Perspective Invariant Dissociaton Hash Method for Remotely sensed Imagery Plusieurs Target Classification

Zhifeng Zhang, Xiao Cui, Xiaohui Ji
2021 IEEE Access  
In order to verify the validity and universality of our method, two different data sets were used for experiments, in which different hash methods and different classification methods are compared.  ...  Plusieurs target classification based on remotely sensed imagery is a hot and difficult-ridden topic in recent years.  ...  In terms of the feature selection of data, internationally recognized Gist features are used [36] .  ... 
doi:10.1109/access.2021.3057165 fatcat:zclo7dmoxjcwrdinqfnvfk5wmu

A Comprehensive Review: Classification Techniques on Hyperspectral Remote Sensing

Purwadi, Nanna Suryana, Universitas Amikom Purwokerto, Purwokerto, Indonesia
2019 International Journal of Advanced Trends in Computer Science and Engineering  
Classification is a big challenge in remote sensing because of many factors that affect its success.  ...  This is because the hyperspectral image can easily distinguish between several materials that share the same spectral features.  ...  Science, Universitas Amikom Purwokerto and Pusat Teknologi Pengkomputeran Termaju (C-ACT), Fakulti Teknologi Maklumat dan Komunikasi (FTMK), Universiti Teknikal Malaysia Melaka (UTeM) for their assistance in  ... 
doi:10.30534/ijatcse/2019/3181.52019 fatcat:3kbgewtygra75amchskvcnwopq
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