Singular Value Decomposition and Principal Component Analysis in Face Images Recognition and FSVDR of Faces

Fronckova, Katerina; Pražák, Pavel; Slaby, Antonin

doi:10.1007/978-3-319-99996-8_10

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…The method is based on the study made by Manohar et al [16] where SVD coupled with QR pivoting is used to optimize the sensor placement for signal reconstruction based on features extracted from a database. SVD can be found in the literature for various uses such as face recognition analysis [17] , [18] , [19] or for information reconstruction from sparse data in fluid dynamics [20] , [21] . Regarding QR, this matrix factorization has proven to be useful in data science applications, particularly for dimension reduction [22] , [23] .…”

Section: Introductionmentioning

confidence: 99%

Application of the singular value and pivoted QR decompositions to reduce experimental efforts in compressor characterization

Tiseira

Plá

Bares

et al. 2022

Heliyon

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Section: Introductionmentioning

confidence: 99%

Application of the singular value and pivoted QR decompositions to reduce experimental efforts in compressor characterization

Tiseira

Plá

Bares

et al. 2022

Heliyon

View full text Add to dashboard Cite

“…The method is based on the study made by Manohar et al [16] where SVD coupled with QR pivoting is used to optimize the sensor placement for signal reconstruction based on features extracted from a database. SVD can be found in the literature for various uses such as face recognition analysis [17,18,19] or for information reconstruction from sparse data in fluid dynamics [20,21]. Regarding QR, this matrix factorization has proven to be useful in data science applications, particularly for dimension reduction [22,23].…”

Section: Introductionmentioning

confidence: 99%

Application of the Singular Value and Pivoted QR Decompositions to Reduce Experimental Efforts in Compressor Characterization

Tiseira

Plá²,

Bares

et al. 2022

SSRN Journal

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Analysing Supercomputer Nodes Behaviour with the Latent Representation of Deep Learning Models

Molan

Borghesi

Benini

et al. 2022

Euro-Par 2022: Parallel Processing

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Anomaly detection systems are vital in ensuring the availability of modern High-Performance Computing (HPC) systems, where many components can fail or behave wrongly. Building a data-driven representation of the computing nodes can help with predictive maintenance and facility management. Luckily, most of the current supercomputers are endowed with monitoring frameworks that can build such representations in conjunction with Deep Learning (DL) models. In this work, we propose a novel semi-supervised DL approach based on autoencoder networks and clustering algorithms (applied to the latent representation) to build a digital twin of the computing nodes of the system. The DL model projects the node features into a lower-dimensional space. Then, clustering is applied to capture and reveal underlying, non-trivial correlations between the features. The extracted information provides valuable insights for system administrators and managers, such as anomaly detection and node classification based on their behaviour and operative conditions. We validated the approach on 240 nodes from the Marconi 100 system, a Tier-0 supercomputer located in CINECA (Italy), considering a 10-month period.

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Singular Value Decomposition and Principal Component Analysis in Face Images Recognition and FSVDR of Faces

Cited by 5 publications

References 14 publications

Application of the singular value and pivoted QR decompositions to reduce experimental efforts in compressor characterization

Application of the singular value and pivoted QR decompositions to reduce experimental efforts in compressor characterization

Application of the Singular Value and Pivoted QR Decompositions to Reduce Experimental Efforts in Compressor Characterization

Analysing Supercomputer Nodes Behaviour with the Latent Representation of Deep Learning Models

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