Search for dissertations about: "high dimensional data"

Showing result 1 - 5 of 451 swedish dissertations containing the words high dimensional data.

  1. 1. Algorithmically Guided Information Visualization : Explorative Approaches for High Dimensional, Mixed and Categorical Data

    Author : Sara Johansson Fernstad; Mikael Jern; Jimmy Johansson; Jane Shaw; Matthew O. Ward; Linköpings universitet; []
    Keywords : NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; Information visualization; data mining; high dimensional data; categorical data; mixed data;

    Abstract : Facilitated by the technological advances of the last decades, increasing amounts of complex data are being collected within fields such as biology, chemistry and social sciences. The major challenge today is not to gather data, but to extract useful information and gain insights from it. READ MORE

  2. 2. Order in the random forest

    Author : Isak Karlsson; Henrik Boström; Lars Asker; Pierre Geurts; Stockholms universitet; []
    Keywords : NATURAL SCIENCES; NATURVETENSKAP; Machine learning; random forest; ensemble; time series; data series; sequential data; sparse data; high-dimensional data; data- och systemvetenskap; Computer and Systems Sciences;

    Abstract : In many domains, repeated measurements are systematically collected to obtain the characteristics of objects or situations that evolve over time or other logical orderings. Although the classification of such data series shares many similarities with traditional multidimensional classification, inducing accurate machine learning models using traditional algorithms are typically infeasible since the order of the values must be considered. READ MORE

  3. 3. Big Data Analytics for eMaintenance : Modeling of high-dimensional data streams

    Author : Liangwei Zhang; Luleå tekniska universitet; []
    Keywords : ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Drift och underhållsteknik; Operation and Maintenance;

    Abstract : Big Data analytics has attracted intense interest from both academia and industry recently for its attempt to extract information, knowledge and wisdom from Big Data. In industry, with the development of sensor technology and Information & Communication Technologies (ICT), reams of high-dimensional data streams are being collected and curated by enterprises to support their decision-making. READ MORE

  4. 4. Big Data Analytics for Fault Detection and its Application in Maintenance

    Author : Liangwei Zhang; Ramin Karim; Janet Lin; Benoît Iung; Luleå tekniska universitet; []
    Keywords : ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Big Data analytics; eMaintenance; fault detection; high-dimensional data; stream data mining; nonlinear data; Drift och underhållsteknik; Operation and Maintenance;

    Abstract : Big Data analytics has attracted intense interest recently for its attempt to extract information, knowledge and wisdom from Big Data. In industry, with the development of sensor technology and Information & Communication Technologies (ICT), reams of high-dimensional, streaming, and nonlinear data are being collected and curated to support decision-making. READ MORE

  5. 5. Factor-Augmented Forecasting for High-Dimensional Data

    Author : Ying Pang; Martin Sköld; Martin Singull; Stockholms universitet; []
    Keywords : NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; High-Dimensional Data; Factor-Augmented Forecasting; Principal Component; Lasso; Cross Validation; Multi-Level Factor; Predictive Performance; matematisk statistik; Mathematical Statistics;

    Abstract : In this thesis, we take a critical look at the factor-augmented forecast models, when a large number of time series variables available can provide the vital information for prediction. We discuss how to describe the commonality and idiosyncrasy of high-dimensional data by a handful of factors in various levels, and how to improve the predictive performance using these factors as augmented predictors. READ MORE