Robust Principal Component Analysis of Data with Missing Values
Kärkkäinen, T., & Saarela, M. (2015). Robust Principal Component Analysis of Data with Missing Values. In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition : Proceedings of the 11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015 (pp. 140-154). Springer International Publishing. Lecture Notes in Computer Science, 9166. https://doi.org/10.1007/978-3-319-21024-7_10
Published inLecture Notes in Computer Science
© Springer International Publishing Switzerland 2015. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
Principal component analysis is one of the most popular machine learning and data mining techniques. Having its origins in statistics, principal component analysis is used in numerous applications. However, there seems to be not much systematic testing and assessment of principal component analysis for cases with erroneous and incomplete data. The purpose of this article is to propose multiple robust approaches for carrying out principal component analysis and, especially, to estimate the relative importances of the principal components to explain the data variability. Computational experiments are first focused on carefully designed simulated tests where the ground truth is known and can be used to assess the accuracy of the results of the different methods. In addition, a practical application and evaluation of the methods for an educational data set is given.
PublisherSpringer International Publishing
Parent publication ISBN978-3-319-21024-7
ConferenceInternational conference on machine learning and data mining
Is part of publicationMachine Learning and Data Mining in Pattern Recognition : Proceedings of the 11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015
NB.Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 9166)
Publication in research information system
MetadataShow full item record
Showing items with similar title or keywords.
Single-trial-based temporal principal component analysis on extracting event-related potentials of interest for an individual subject Zhang, Guanghui; Li, Xueyan; Lu, Yingzhi; Tiihonen, Timo; Chang, Zheng; Cong, Fengyu (Elsevier, 2023)Background: Temporal principal component analysis (tPCA) has been widely used to extract event-related potentials (ERPs) at group level of multiple subjects ERP data and it assumes that the underlying factor loading is ...
Extracting conditionally heteroskedastic components using independent component analysis Miettinen, Jari; Matilainen, Markus; Nordhausen, Klaus; Taskinen, Sara (Wiley-Blackwell, 2020)In the independent component model, the multivariate data are assumed to be a mixture of mutually independent latent components. The independent component analysis (ICA) then aims at estimating these latent components. In ...
Student agency analytics : learning analytics as a tool for analysing student agency in higher education Jääskelä, Päivikki; Heilala, Ville; Kärkkäinen, Tommi; Häkkinen, Päivi (Taylor & Francis, 2021)This paper presents a novel approach and a method of learning analytics to study student agency in higher education. Agency is a concept that holistically depicts important constituents of intentional, purposeful, and ...
Application of a Knowledge Discovery Process to Study Instances of Capacitated Vehicle Routing Problems Kärkkäinen, Tommi; Rasku, Jussi (Springer, 2020)Vehicle Routing Problems (VRP) are computationally challenging, constrained optimization problems, which have central role in logistics management. Usually different solvers are being developed and applied for different ...