TY  -  
T1  - Graph embedding with data uncertainty 
SN  -  /  
UR  - http://hdl.handle.net/10138/343150 
T3  - IEEE Access 
A1  - Laakom, Firas; Raitoharju, Jenni; Passalis, Nikolaos; Iosifidis, Alexandros; Gabbouj, Moncef 
A2  -  
PB  - Institute of Electrical and Electronics Engineers (IEEE) 
Y1  - 2022 
LA  - en 
AB  - Spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines. The main aim is to learn a meaningful low dimensional embedding of the data. However, most subspace learning methods do not take into consideration possible measurement inaccuracies or artifacts that can lead to data with high uncertainty. Thus, learning directly from raw data can be misleading and can negatively impact the accuracy. In this paper, we propose to model artifacts in training da... 
VO  - 10 
IS  -  
SP  -  
OP  -  
KW  - epävarmuus; mallit; tietomallit; optimointi; normaalijakauma; oppiminen; koneoppiminen; 113 Tietojenkäsittely ja informaatiotieteet; uncertainty; data models; principal component analysis; optimization; Gaussian distribution; eigenvalues and eigenfunctions; training data; feature extraction; graph theory; learning (artificial intelligence); data uncertainty; common data preprocessing step; machine learning pipelines; meaningful low dimensional embedding; subspace learning methods; consideration possible measurement inaccuracies; raw data; probability distributions; original data point; graph embedding framework; graph embedding; subspace learning; dimensionality reduction; uncertainty estimation; spectral learning; models; models (objects); modelling; modelling (creation related to information); data models; optimisation; normal distribution; learning; machine learning; 113 Computer and information sciences 
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ER  -