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Ulotteisuuden pienentäminen pääkomponenttianalyysilla liikeanalyysissa

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Authors
Lempinen, Aleksander
Date
2019
Discipline
TietotekniikkaMathematical Information Technology
Copyright
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.

 
Liikeanalyysissa tuotetaan paljon korkeaulotteista mittausdataa, jonka käsittelyyn tarvitaan usean muuttujan menetelmiä. Suuret datamäärät johtavat myös siihen, että menetelmät tarvitsevat enemmän laskentatehoa. Ohjaamattomaan oppimiseen kuuluva ulotteisuuden pienentämisen menetelmä pääkomponenttianalyysi on laajasti käytössä liikeanalyysissa. Tässä tutkielmassa käsitellään pääkomponenttianalyysin hyödyntämistä kliinisessä liikeanalyysitutkimuksessa.
 
Human locomotion research or gait analysis measurements produce large amounts of high-dimensional data, which requires a multivariate approach. Large amounts of data also require more computational resources. Pricipal component analysis which is an unsupervised dimensionality reduction method is in widespread use in gait analysis. This thesis addresses applications of principal component analysis in clinical gait analysis.
 
Keywords
pca principal component analysis machine learning gait analysis human locomotion pääkomponenttianalyysi liikeanalyysi data koneoppiminen big data tiedonlouhinta faktorianalyysi
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http://urn.fi/URN:NBN:fi:jyu-201904252264

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