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Deep Archetypal Analysis [article]

Sebastian Mathias Keller, Maxim Samarin, Mario Wieser, Volker Roth
2020 arXiv   pre-print
"Deep Archetypal Analysis" generates latent representations of high-dimensional datasets in terms of fractions of intuitively understandable basic entities called archetypes.  ...  In the unsupervised setting we show how "Deep AA" is used on CelebA to identify archetypal faces.  ...  Deep Archetypal Analysis Deep Archetypal Analysis can then be formulated in the following way. For the sampling of in Eq. (9) the probabilistic AA approach as in Eq.  ... 
arXiv:1901.10799v2 fatcat:nhckkcnzqfbbfjz33zmkskynnm

Learning Extremal Representations with Deep Archetypal Analysis

Sebastian Mathias Keller, Maxim Samarin, Fabricio Arend Torres, Mario Wieser, Volker Roth
2020 International Journal of Computer Vision  
As mixing of archetypes is performed directly on the input data, linear Archetypal Analysis requires additivity of the input, which is a strong assumption unlikely to hold e.g. in case of image data.  ...  By introducing the distance-dependent archetype loss, the linear archetype model can be integrated into the latent space of a deep variational information bottleneck and an optimal representation, together  ...  Deep Archetypal Analysis Deep Archetypal Analysis can then be formulated in the following way.  ... 
doi:10.1007/s11263-020-01390-3 pmid:34720403 pmcid:PMC8550171 fatcat:x5bnaoyflzg2zpyqnzdkbxpbhm

Learning Extremal Representations with Deep Archetypal Analysis [article]

Sebastian Mathias Keller, Maxim Samarin, Fabricio Arend Torres, Mario Wieser, Volker Roth
2020 arXiv   pre-print
The reformulation of linear Archetypal Analysis as deep variational information bottleneck, allows the incorporation of arbitrarily complex side information during training.  ...  unknown archetypes can be learned end-to-end.  ...  Deep Archetypal Analysis Deep Archetypal Analysis can then be formulated in the following way.  ... 
arXiv:2002.00815v1 fatcat:acooaq7xvnfj5kexq72upbpitu

Convex representations using deep archetypal analysis for predicting glaucoma

Anshul Thakur, Michael Goldbaum, Siamak Yousefi
2020 IEEE Journal of Translational Engineering in Health and Medicine  
Deep archetypal analysis models may impact clinical practice in effectively identifying at-risk glaucoma patients well prior to disease development.  ...  Methods: We developed a deep archetypal analysis to identify patterns of glaucomatous vision loss, and then projected visual fields over the identified patterns.  ...  The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  ... 
doi:10.1109/jtehm.2020.2982150 pmid:32596065 pmcid:PMC7316201 fatcat:4qsp3z7e2jhshptydraptz3zni

Non-linear Archetypal Analysis of Single-cell RNA-seq Data by Deep Autoencoders [article]

Yuge Wang, Hongyu Zhao
2021 bioRxiv   pre-print
We first show that scAAnet outperforms existing methods for archetypal analysis across different metrics through simulations.  ...  We introduce scAAnet, an autoencoder for single-cell non-linear archetypal analysis, to identify GEPs and infer the relative activity of each GEP across cells.  ...  A relatively new framework for non-linear archetypal analysis borrows ideas from the field of deep learning.  ... 
doi:10.1101/2021.09.17.460824 fatcat:dueyzak2dnbshevewruoelcbqu

ASe: Acoustic Scene Embedding Using Deep Archetypal Analysis and GMM

Pulkit Sharma, Vinayak Abrol, Anshul Thakur
2018 Interspeech 2018  
The proposed deep factorization model is based on archetypal analysis, a form of convex NMF, which has been shown to be well suited for audio analysis.  ...  In this paper, we propose a deep learning framework which combines the generalizability of Gaussian mixture models (GMM) and discriminative power of deep matrix factorization to learn acoustic scene embedding  ...  Leveraging the recent advancements in deep matrix factorization (DMF) [9, 10] , the proposed deep framework is based on archetypal analysis (AA) [11] , a form of convex NMF, which has been shown to be  ... 
doi:10.21437/interspeech.2018-1481 dblp:conf/interspeech/SharmaAT18 fatcat:7go4tzcedffg5abcmpz6avrs44

Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders

Yuge Wang, Hongyu Zhao, Mingyao Li
2022 PLoS Computational Biology  
We first show that scAAnet outperforms existing methods for archetypal analysis across different metrics through simulations.  ...  We introduce scAAnet, an autoencoder for single-cell non-linear archetypal analysis, to identify GEPs and infer the relative activity of each GEP across cells.  ...  A relatively new framework for non-linear archetypal analysis borrows ideas from the field of deep learning.  ... 
doi:10.1371/journal.pcbi.1010025 pmid:35363784 pmcid:PMC9007392 fatcat:voa6art7mngsnnvvba5bblenue

Seasonal and annual reoccurrence in betaproteobacterial ammonia-oxidizing bacterial population structure

Nicholas J. Bouskill, Damien Eveillard, Gregory O'Mullan, George A. Jackson, Bess B. Ward
2010 Environmental Microbiology  
Using a combination of a non-weighted discrimination analysis and principal components analysis of community composition data obtained from functional gene microarrays, it was found that co-varying AOB  ...  Among the most notable patterns were correlations of AOB archetypes with temperature, DON and ammonium concentrations.  ...  (A) Surface; (B) middle; (C) deep; (D) sediment. Fig. S6. Temporal classification of co-varying archetypes based on K-means discrimination analysis at the (A) surface and (B) deep depths.  ... 
doi:10.1111/j.1462-2920.2010.02362.x pmid:21054735 fatcat:i4bz6xzj2zclbgvfe72esukddq

Deep Convex Representations: Feature Representations for Bioacoustics Classification

Anshul Thakur, Vinayak Abrol, Pulkit Sharma, Padmanabhan Rajan
2018 Interspeech 2018  
Archetypal analysis, a form of convex non-negative matrix factorization, is used for acoustic modelling at each level of this framework.  ...  In this paper, a deep convex matrix factorization framework is proposed for bioacoustics classification.  ...  Future work may include the application of deep convex representation for other audio classification tasks. Figure 1 : 1 Illustration of the archetypal analysis based deep matrix factorization.  ... 
doi:10.21437/interspeech.2018-1705 dblp:conf/interspeech/ThakurASR18 fatcat:wyivhzgglbh3lgejzz7b3kn7um

Archetypal Symbolization in Psychodrawings

I. Serhata
2021 Science and Education a New Dimension  
In groups of ASPC there often arise situations, in the process of deep psychocorrection, a combination of the analysis of psychodrawings with dreams.  ...  The problem of symbology decoding, including the archetype, is important in deep cognition (T. Yatsenko). According to E.  ... 
doi:10.31174/send-pp2021-252ix99-19 fatcat:kds226l42ndwra7uqlfazzgnx4

Glaucoma Precognition: Recognizing Preclinical Visual Functional Signs of Glaucoma

Krati Gupta, Anshul Thakur, Michael Goldbaum, Siamak Yousefi
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Deep archetypal analysis (DAA) has recently been proposed as an unsupervised approach for discovering latent structures in data.  ...  However, while a few approaches have used classical archetypal analysis (AA), DAA has not been incorporated in medical image analysis as yet.  ...  Deep Archetypal Analysis (DAA) Archetypal analysis and DAA were introduced for discovering latent factors from high-dimensional data by performing matrix factorization.  ... 
doi:10.1109/cvprw50498.2020.00518 dblp:conf/cvpr/GuptaTGY20 fatcat:qifqq7g4b5hbzejrkpisdj4tzq

THE ARCHETYPE WATER IN SOME FOLKLORE NARRATIONS

T. A. Khitarova, E. G. Khitarova
2017 Filologické vědomosti  
The analysis of a spatial dichotomy of archetypes Height-Bottom is vital.  ...  Immersing in the water goes parallel with danger and mystery, shows reflexion of an archetype Bottom and on the contraryreturning to the earth, home, motherland works as a mirror of archetype Height.  ...  Hence, the analysis of a spatial dichotomy of archetypes Height-Bottom is vital.  ... 
doi:10.24045/fv.2017.2.7 fatcat:ih3b4ggixzfwdb4xt6ftofsbca

Integrated assessment of the spatial distribution and structural dynamics of deep benthic marine communities

Jan Jansen, Piers K. Dunstan, Nicole A. Hill, Philippe Koubbi, Jessica Melbourne‐Thomas, Romain Causse, Craig R. Johnson
2019 Ecological Applications  
Species archetype C is dominant along the shelf break and continental slope, preferring deep and steep habitat.  ...  Unexpectedly, the analysis suggests this species archetype is more likely to occur at low levels of sedimentation (Fig. 3) , which is atypical for the basins.  ... 
doi:10.1002/eap.2065 pmid:31872512 fatcat:mwhpfkf5vfbvdcc2gqkwdmbskm

New insights on the mineralization of dissolved organic matter in central, intermediate, and deep water masses of the northeast North Atlantic

X. A. Álvarez-Salgado, M. Nieto-Cid, M. Álvarez, F. F. Pérez, P. Morin, H. Mercier
2013 Limnology and Oceanography  
An optimum multiparameter (OMP) analysis was applied to the samples collected during a cruise in the northeast North Atlantic with the aim of objectively defining water mass realms and calculating water  ...  mass mixing-weighted average (archetypal) concentrations of dissolved organic carbon (DOC) and nitrogen (DON) and fluorescent dissolved organic matter (FDOM).  ...  The total number of samples, n, included in the OMP analysis was 1937.  ... 
doi:10.4319/lo.2013.58.2.0681 fatcat:ur4wpmhgvjfgzkemxle63foohu

Unsupervised Learning of Artistic Styles with Archetypal Style Analysis [article]

Daan Wynen, Cordelia Schmid, Julien Mairal
2018 arXiv   pre-print
Our method is based on archetypal analysis, which is an unsupervised learning technique akin to sparse coding with a geometric interpretation.  ...  When applied to deep image representations from a collection of artworks, it learns a dictionary of archetypal styles, which can be easily visualized.  ...  Figure 1 : 1 Using deep archetypal style analysis, we can represent an artistic image (a) as a convex combination of archetypes.  ... 
arXiv:1805.11155v2 fatcat:e4cv3kcak5grfb6zkhd32vencm
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