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Deep Variational Canonical Correlation Analysis [article]

Weiran Wang, Xinchen Yan, Honglak Lee, Karen Livescu
2017 arXiv   pre-print
We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear observation models parameterized  ...  by deep neural networks.  ...  As shown in Conclusions We have proposed variational canonical correlation analysis (VCCA), a deep generative method for multi-view representation learning.  ... 
arXiv:1610.03454v3 fatcat:t7kqnghpnzf7dkux537l3s6jna

Deep Generalized Canonical Correlation Analysis [article]

Adrian Benton, Huda Khayrallah, Biman Gujral, Dee Ann Reisinger, Sheng Zhang, Raman Arora
2017 arXiv   pre-print
We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally  ...  While methods for nonlinear two-view representation learning (Deep CCA, (Andrew et al., 2013)) and linear many-view representation learning (Generalized CCA (Horst, 1961)) exist, DGCCA is the first CCA-style  ...  Here we present Deep Generalized Canonical Correlation Analysis (DGCCA).  ... 
arXiv:1702.02519v2 fatcat:3omuyay7nbadzibh3jtwz6yfhy

Deep Generalized Canonical Correlation Analysis

Adrian Benton, Huda Khayrallah, Biman Gujral, Dee Ann Reisinger, Sheng Zhang, Raman Arora
2019 Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)  
We present Deep Generalized Canonical Correlation Analysis (DGCCA) -a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally  ...  of nonlinear (deep) representation learning with the statistical power of incorporating information from many sources, or views.  ...  We present Deep Generalized Canonical Correlation Analysis (DGCCA).  ... 
doi:10.18653/v1/w19-4301 dblp:conf/rep4nlp/BentonKGRZA19 fatcat:yw7uq3kbpfe7dbz6k3q54zuhdm

Dynamically-Scaled Deep Canonical Correlation Analysis [article]

Tomer Friedlander, Lior Wolf
2022 arXiv   pre-print
Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them.  ...  We introduce a novel dynamic scaling method for training an input-dependent canonical correlation model.  ...  Related Work Canonical Correlation Analysis (CCA) [Harold, 1936] is a method for finding linear projections of two views, such that the projections are maximally correlated, while the projections within  ... 
arXiv:2203.12377v2 fatcat:iwfyps76vvbaxikemp634zwfx4

Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion [article]

Yiqi Zhong, Cho-Ying Wu, Suya You, Ulrich Neumann
2020 arXiv   pre-print
We extend canonical correlation analysis to a 2D domain and formulate it as one of our training objectives (i.e. 2d deep canonical correlation, or "2D2CCA loss").  ...  In this paper, we propose our Correlation For Completion Network (CFCNet), an end-to-end deep learning model that uses the correlation between two data sources to perform sparse depth completion.  ...  To learn the relationship between two modalities, we propose a 2D deep canonical correlation analysis (2D 2 CCA).  ... 
arXiv:1906.08967v3 fatcat:i5qpam75cndvxbw5fpedaffhje

Acoustic Feature Learning via Deep Variational Canonical Correlation Analysis [article]

Qingming Tang, Weiran Wang, Karen Livescu
2017 arXiv   pre-print
We use deep variational canonical correlation analysis (VCCA), a recently proposed deep generative method for multi-view representation learning.  ...  A popular class of methods in this area is based on canonical correlation analysis (CCA, [7] ) and its nonlinear extensions [8, 9, 10] .  ...  In this paper, we explore a recently proposed deep generative variant of CCA, deep variational CCA (VCCA) [11] , for multi-view acoustic feature learning.  ... 
arXiv:1708.04673v2 fatcat:ndvf7v7d3faelh7xun6fzcqfey

Variational Inference for Deep Probabilistic Canonical Correlation Analysis [article]

Mahdi Karami, Dale Schuurmans
2020 arXiv   pre-print
In this paper, we propose a deep probabilistic multi-view model that is composed of a linear multi-view layer based on probabilistic canonical correlation analysis (CCA) description in the latent space  ...  together with deep generative networks as observation models.  ...  On Deep Multi-View Representation Learning. Icml, 37, 2015. Weiran Wang, Xinchen Yan, Honglak Lee, and Karen Livescu. Deep variational canonical correlation analysis.  ... 
arXiv:2003.04292v1 fatcat:hzxjrysrebacbaivca5rpp335q

Multi-Modal Sentiment Analysis Using Deep Canonical Correlation Analysis

Zhongkai Sun, Prathusha K. Sarma, William Sethares, Erik P. Bucy
2019 Interspeech 2019  
Individual features derived from the three views are combined into a multi-modal embedding using Deep Canonical Correlation Analysis (DCCA) in two ways i) One-Step DCCA and ii) Two- Step DCCA.  ...  Deep Canonical Correlation Analysis (DCCA) Classic Canonical Correlation Analysis (CCA) [14] is a statistical technique used to find a linear subspace in which two sets of random variables with finite  ...  Methods This section briefly reviews Deep Canonical Correlation Analysis (DCCA) and outlines the methods used to obtain unimodal features.  ... 
doi:10.21437/interspeech.2019-2482 dblp:conf/interspeech/SunSSB19 fatcat:g5bmatn74bevpdgx3hzq4v4qbm

ℓ_0-based Sparse Canonical Correlation Analysis [article]

Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger
2021 arXiv   pre-print
Canonical Correlation Analysis (CCA) models are powerful for studying the associations between two sets of variables.  ...  We further propose ℓ_0-Deep CCA for solving the problem of non-linear sparse CCA by modeling the correlated representations using deep nets.  ...  They proposed Deep Canonical Correlation Analysis (DCCA) which extracts two nonlinear transformations of X and Y with maximal correlation.  ... 
arXiv:2010.05620v2 fatcat:nzc2ielsenbnbkwul77p4ciodq

Communication-Efficient Distributed Linear and Deep Generalized Canonical Correlation Analysis [article]

Sagar Shrestha, Xiao Fu
2021 arXiv   pre-print
Classic and deep learning-based generalized canonical correlation analysis (GCCA) algorithms seek low-dimensional common representations of data entities from multiple "views" (e.g., audio and image) using  ...  This work puts forth a communication-efficient distributed framework for both linear and deep GCCA under the maximum variance (MAX-VAR) paradigm.  ...  columns of X 1 Q 1 and X 2 Q 2 -which reflects the name "canonical correlation analysis".  ... 
arXiv:2109.12400v1 fatcat:az473vazlvfxloy2jf247757ui

SDGCCA: Supervised Deep Generalized Canonical Correlation Analysis for Multi-omics Integration [article]

Jeongyoung Hwang
2022 arXiv   pre-print
SDGCCA addresses the limitations of other canonical correlation analysis (CCA)-based models (e.g., deep CCA, deep generalized CCA) by considering complex/nonlinear cross-data correlations and discriminating  ...  We propose a novel method of multi-omics integration called supervised deep generalized canonical correlation analysis (SDGCCA) for modeling correlation structures between nonlinear multi-omics manifolds  ...  DCCA A deep canonical correlation analysis (DCCA) [13] is used to solve the limitations of CCA that extracts only the linear relationship.  ... 
arXiv:2204.09045v1 fatcat:cd74ybl6jjb3jgaz22yczwyi2y

Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis [article]

Wei Liu, Jie-Lin Qiu, Wei-Long Zheng, Bao-Liang Lu
2019 arXiv   pre-print
In this paper, we introduce deep canonical correlation analysis (DCCA) to multimodal emotion recognition.  ...  The basic idea behind DCCA is to transform each modality separately and coordinate different modalities into a hyperspace by using specified canonical correlation analysis constraints.  ...  Deep Canonical Correlation Analysis In this paper, we introduce deep canonical correlation analysis (DCCA) to multimodal emotion recognition.  ... 
arXiv:1908.05349v1 fatcat:bng436um4jhvrlmmvxcbpundey

Triphone State-Tying via Deep Canonical Correlation Analysis

Weiran Wang, Hao Tang, Karen Livescu
2016 Interspeech 2016  
Our method first learns low-dimensional embeddings of context-dependent phones using deep canonical correlation analysis.  ...  Label embedding via deep CCA We first review deep canonical correlation analysis (deep CCA), which has been one of the most successful methods for unsupervised learning of representations (features) from  ...  The algorithm we use to learn the embedding is deep canonical correlation analysis [3, 4] , which projects acoustic inputs and triphone labels into a common subspace using deep neural networks (DNNs),  ... 
doi:10.21437/interspeech.2016-1300 dblp:conf/interspeech/WangTL16 fatcat:rznwp7g3o5aezl7swgc2pukjzi

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability [article]

Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein
2017 arXiv   pre-print
We propose a new technique, Singular Vector Canonical Correlation Analysis (SVCCA), a tool for quickly comparing two representations in a way that is both invariant to affine transform (allowing comparison  ...  and Canonical Correlation Analysis (CCA) [5] , as a powerful method for analyzing deep representations.  ...  Within this formalism, we introduce Singular Vector Canonical Correlation Analysis (SVCCA) as a method for analysing representations.  ... 
arXiv:1706.05806v2 fatcat:o6rrzfxxevd45cexk5bdrlzhk4

Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis [article]

Sarfaraz Hussein, Pujan Kandel, Juan E. Corral, Candice W. Bolan, Michael B. Wallace, Ulas Bagci
2018 arXiv   pre-print
At the final step, we employ canonical correlation analysis (CCA) to perform a fusion operation at the feature level, leading to discriminative canonical correlation features.  ...  Then, we present a CNN to obtain deep feature representation corresponding to each MRI modality (T1-weighted and T2-weighted).  ...  representation using Canonical Correlation Analysis (CCA) to obtain better discrimination between normal and subjects with IPMN.  ... 
arXiv:1710.09779v3 fatcat:jl7wglzo3fhfznmkatusfp3qhi
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