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Unsupervised Correlation Analysis

Yedid Hoshen, Lior Wolf
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
We introduce a new method Unsupervised Correlation Analysis (UCA), which requires no prior correspondences between the two domains.  ...  One of the most popular methods for linking between domains is Canonical Correlation Analysis (CCA). All current CCA algorithms require correspondences between the views.  ...  Method In this section, we describe Unsupervised Correlation Analysis (UCA). Similarly to CCA, the method projects the data from the two domains into a shared space.  ... 
doi:10.1109/cvpr.2018.00350 dblp:conf/cvpr/HoshenW18 fatcat:hk6lccjsprggdnfgstffb2ngma

Unsupervised Correlation Analysis [article]

Yedid Hoshen, Lior Wolf
2018 arXiv   pre-print
We introduce a new method Unsupervised Correlation Analysis (UCA), which requires no prior correspondences between the two domains.  ...  One of the most popular methods for linking between domains is Canonical Correlation Analysis (CCA). All current CCA algorithms require correspondences between the views.  ...  Method In this section, we describe Unsupervised Correlation Analysis (UCA). Similarly to CCA, the method projects the data from the two domains into a shared space.  ... 
arXiv:1804.00347v1 fatcat:7yv6jklvrfaxtoinb5kdnb6oou

Spectral denoising for unsupervised analysis of correlated ionic transport [article]

Nicola Molinari and Yu Xie and Ian Leifer and Aris Marcolongo and Mordechai Kornbluth and Boris Kozinsky
2021 arXiv   pre-print
This allows to systematically reduce the uncertainty and accelerate computations of ionic conductivity in systems with a steady-state correlation structure.  ...  Computation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive as one cannot rely on the affordable mean square displacement approach.  ...  The spectral analysis of the position-position covariance matrix is shown to be an unsupervised way to uncover stable collective diffusion modes and particle clusters, automatically revealing the microscopic  ... 
arXiv:2007.08734v2 fatcat:mpfdkgfbwjbdve4ezvepekp6xm

Robust Matrix Elastic Net based Canonical Correlation Analysis: An Effective Algorithm for Multi-View Unsupervised Learning [article]

Peng-Bo Zhang, Zhi-Xin Yang
2017 arXiv   pre-print
This paper presents a robust matrix elastic net based canonical correlation analysis (RMEN-CCA) for multiple view unsupervised learning problems, which emphasizes the combination of CCA and the robust  ...  The RMEN-CCA leverages the strength of the RMEN to distill naturally meaningful features without any prior assumption and to measure effectively correlations between different 'views'.  ...  Canonical Correlation Analysis (CCA) is a classical and powerful unsupervised learning approach for the multi-view learning problem.  ... 
arXiv:1711.05068v2 fatcat:omgbvuz6urcf7etc7tov3s55tq

Unsupervised learning of acoustic features via deep canonical correlation analysis

Weiran Wang, Raman Arora, Karen Livescu, Jeff A. Bilmes
2015 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
analysis (CCA).  ...  shown that, when both acoustic and articulatory training data are available, it is possible to improve phonetic recognition accuracy by learning acoustic features from this multiview data with canonical correlation  ...  The idea of feature learning using multi-view data has been explored previously using canonical correlation analysis (CCA) [4] and its nonlinear extension kernel CCA (KCCA) [5, 6] .  ... 
doi:10.1109/icassp.2015.7178840 dblp:conf/icassp/WangALB15 fatcat:kmltgwppv5g3pokv2oedvztpau

Hunting attacks in the dark: clustering and correlation analysis for unsupervised anomaly detection

Johan Mazel, Pedro Casas, Romain Fontugne, Kensuke Fukuda, Philippe Owezarski
2015 International Journal of Network Management  
Unsupervised detection is accomplished by means of robust clustering techniques, combining sub-space clustering with correlation analysis to blindly identify anomalies.  ...  Finally, we compare the performance of the unsupervised detection against different previously used unsupervised detection techniques, as well as against multiple anomaly detectors used in MAWILab.  ...  Unsupervised detection is accomplished by means of robust clustering techniques, combining sub-space clustering with correlation analysis to blindly identify anomalies.  ... 
doi:10.1002/nem.1903 fatcat:h5yesz62vjhzvpdvq3ejmlllde

Enhancing unsupervised canonical correlation analysis-based frequency detection of SSVEPs by incorporating background EEG

Masaki Nakanishi, Yijun Wang, Yu-Te Wang, Yasue Mitsukura, Tzyy-Ping Jung
2014 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
In SSVEP-based BCIs, Canonical Correlation Analysis (CCA) has been widely used to detect frequencycoded SSVEPs due to its high efficiency and robustness.  ...  The proposed method defined a normalized canonical correlation coefficient, the ratio of the canonical correlation coefficient for SSVEPs to the mean of the canonical correlation coefficients for background  ...  This study therefore proposes a new unsupervised method to derive normalized canonical correlation coefficients, which are defined as canonical correlation coefficients for SSVEPs divided by the mean of  ... 
doi:10.1109/embc.2014.6944267 pmid:25570635 dblp:conf/embc/NakanishiWWMJ14 fatcat:l5huhr3p5zcoxbet524jf74b7i

Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images [article]

Magauiya Zhussip, Shakarim Soltanayev, Se Young Chun
2019 arXiv   pre-print
Recently, Stein's unbiased risk estimator (SURE) has been applied to unsupervised training of deep neural network Gaussian denoisers that outperformed classical non-deep learning based denoisers and yielded  ...  Here, we propose an extended SURE (eSURE) to train deep denoisers with correlated pairs of noise realizations per image and applied it to the case with two uncorrelated realizations per image to achieve  ...  However, Noise2Noise did not yield good performance with correlated noisy realizations as predicted based on our theoretical analysis. ( a ) a Ground Truth (b) BM3D / 25.40 dB (c) SURE / 25.83 dB (d) N2N  ... 
arXiv:1902.02452v2 fatcat:7n354pswhredxkigyvpubbu54y

Unsupervised speaker normalization using canonical correlation analysis

Y. Ariki, M. Sakuragi
Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181)  
To solve this problem, we construct the speaker subspace for an individual speaker and correlate them by o-space canonical correlation analysis between the standard speaker and input speaker.  ...  method which automatically segments the speech data into phoneme data by Viterbi decoding algorithm and then associates the mean feature vectors of phoneme data by o-space canonical correlation analysis  ...  UNSUPERVISED NORMALIZATION Problems in O-space Canonical Correlation Analysis The O-space canonical correlation analysis described in the previous section has the following two problems; (1) Association  ... 
doi:10.1109/icassp.1998.674375 dblp:conf/icassp/ArikiS98 fatcat:an3x4mxfandnvn33n3u7cwkg3y

Unsupervised environmental sound source localization and acoustic image analysis of geometry-optimized spherical microphone arrays using the generalized cross- correlation

Lucas Henrique Teixeira Carneiro, Alain Berry
2020
Otsus' and Bradleys' methods are both non-parametric and unsupervised automatic image segmentation methods based on discriminant and integrative analysis, respectively.  ...  The discriminant analysis assumes that the gray-level histogram has bimodal distribution.  ... 
doi:10.48465/fa.2020.0863 fatcat:uskpczpdujfrhnw6hblvl3tqy4

Application of Feature Selection for Unsupervised Learning in Prosecutors' Office [chapter]

Peng Liu, Jiaxian Zhu, Lanjuan Liu, Yanhong Li, Xuefeng Zhang
2005 Lecture Notes in Computer Science  
In this paper, we propose a novel methodology ULAC (Feature Selection for Unsupervised Learning Based on Attribute Correlation Analysis and Clustering Algorithm) to identify important features for unsupervised  ...  We also apply ULAC into prosecutors' office to solve the real world application for unsupervised learning.  ...  Correlation Analysis and Clustering Algorithm)(Fig.2).  ... 
doi:10.1007/11540007_5 fatcat:cxmmyk4fobanblks55ipj4qhji

Bacterial community comparisons by taxonomy-supervised analysis independent of sequence alignment and clustering

W. J. Sul, J. R. Cole, E. d. C. Jesus, Q. Wang, R. J. Farris, J. A. Fish, J. M. Tiedje
2011 Proceedings of the National Academy of Sciences of the United States of America  
We compared this method with a commonly used clustering approach based on sequence similarity (taxonomy-unsupervised analysis).  ...  This process, termed "taxonomy-unsupervised analysis," originates from the distribution of 16S rRNA gene sequences into OTUs.  ...  rather than with the taxonomy-unsupervised analysis (Figs. 3 and 4) .  ... 
doi:10.1073/pnas.1111435108 pmid:21873204 pmcid:PMC3167511 fatcat:geyat7xylnc5jn4eewlsr7o2ty

Tensor decomposition--based unsupervised feature extraction for integrated analysis of TCGA data on microRNA expression and promoter methylation of genes in ovarian cancer [article]

Y-h. Taguchi, Ka-Lok Ng
2018 bioRxiv   pre-print
Integrated analysis of epigenetic profiles is important but difficult.  ...  The expression levels of the seven miRNAs and the degrees of promoter methylation of the 241 genes also correlated significantly.  ...  One may still wonder if Student's t test can compete with TD-based unsupervised FE when correlation analysis is restricted to the miRNAs and genes with large differences between controls and treated samples  ... 
doi:10.1101/380071 fatcat:xuc56v2whjc3vkzzrwsdxdhbgy

Visual Feature Space Analysis for Unsupervised Effectiveness Estimation and Feature Engineering

Tobias Schreck, Daniel Keim, Christian Panse
2006 2006 IEEE International Conference on Multimedia and Expo  
We here address the problem of comparative unsupervised feature space analysis.  ...  The results of the analysis are useful for feature selection and engineering.  ...  We verify the correlation between the supervised and the unsupervised FV space metric at squared correlation coefficient R 2 = 0.78.  ... 
doi:10.1109/icme.2006.262671 dblp:conf/icmcs/SchreckKP06 fatcat:ynp4auo37vajflxlb6jjotxa3y

Combination of B-mode and color Doppler mode using mutual information including canonical correlation analysis for breast cancer diagnosis

Tongjai Yampaca, Prabhas Chongstitvatana
2020 Medical ultrasonography  
Aim: This study proposes the combination of B-mode and color Doppler mode using Mutual Information including Canonical Correlation Analysis (MI-CCA) to improve breast cancer diagnosis.Materials and methods  ...  In addition, the unsupervised-PCA was high (AUC 0.91, 95% CI [0.90, 0.91]) and no significant difference was observed with the unsupervised-CCA (AUC 0.90, 95% CI [0.84, 0.90]).  ...  Statistical analysis Experiment 1: Exploration of correlation analysis via Pearson correlation Because the objective of the data fusion method is the strongest correlation between two datasets, the Pearson  ... 
doi:10.11152/mu-2270 pmid:32096788 fatcat:u4qlrmsbhrfshhurc342vlkzh4
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