Kasper Knoblauch Christensen has defended his PhD

Kasper Christensen presented his PhD at April 26th.

Title of the thesis is “Detecting ideas in online communities: Utilizing machine learning and text mining for finding ideas in online communities”. The given topic for the trial lecture was ” What is the future of Big Data in Food, Beverage and Personal Products? – A critical perspective.”

In his thesis, Kasper has explored the possibilities of automatic detection of ideas from online communities.by using text mining and machine learning. He also showed that PLS regression in combination with variable selection can be used to identify the words and phrases that define an idea. He used cases from Lego and beer brewing in his work.

Supervisors:

  • Professor Knut Kvaal, Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
  • Dr. Argic Einar Risvik, Senior Research Scientist, Department of Sensory, Consumer and Innovation, Nofima, Ås, Norway
  • Professor Tormod Næs, Senior Research Scientist, Nofima, Ås, Norway and Department of Food Science, Quality and Technology, Faculty of Life Sciences, University of Copenhagen, Copenhagen, Denmark
  • Dr. Torulf Mollestad, Principal consultant, Altran, Norway, Oslo

 

Evaluation committee:

  • Dr. Hal Macfie, Visiting Professor, Universities of Reading, Nottingham, United Kingdom
  • Professor Per B. Brockhoff, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
  • Associate Professor, Jorge M. Marchetti, Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway

Alessandra Biancolillo awarded PhD

Alessandra Biancolillo defended her PhD thesis at the University of Copenhagen on November 18th. Alessandra has been associated with Nofima’s  strategic program “Multiblock Methods for prediction and interpretation”, developing methodology for analyzing and interpreting several blocks of data sequentially (so-called SO-PLS). The thesis is titled “Method development in the area of multi-block analysis focused on food analysis”. Supervisors have been Tormod Næs (Nofima), Ingrid Måge (Nofima) and Rasmus Bro (University of Copenhagen).

Alessandra did a very good job at the defense. Opponents Thomas Skov (University of Copenhagen), El Mostafa Qannari (Oniris, Nantes) and Lars Nørgaard (Roskilde University) were well prepared and contributed to an interesting and fruitful discussion.

ale_opponents

 

 

Multiblock classification: SO-PLS and LDA

Combining SO-PLS and linear discriminant analysis for multi-block classification

allessandraThe “Combining SO-PLS and linear discriminant analysis for multi-block classification”, written by Alessandra Biancolillo, Ingrid Måge and Tormod Næs was recently published in Chemometrics and Intelligent Laboratory systems. This is the first paper by Ph.D student Alessandra. Congratulations!

Abstract

The aim of the present work is to extend the Sequentially Orthogonalized-Partial Least Squares (SO-PLS) regression method, usually used for continuous output, to situations where classification is the main purpose. For this reason SO-PLS discriminant analysis will be compared with other commonly used techniques such as Partial Least Squares-Discriminant Analysis (PLS-DA) and Multiblock-Partial Least Squares Discriminant Analysis (MB-PLS-DA). In particular we will focus on how multiblock strategies can give better discrimination than by analyzing the individual blocks. We will also show that SO-PLS discriminant analysis yields some valuable interpretation tools that give additional insight into the data. We will introduce some new ways to represent the information, taking into account both interpretation and predictive aspects.

More details

Full reference:

Biancolillo, A., Måge, I., & Næs, T. (2015). Combining SO-PLS and linear discriminant analysis for multi-block classification. Chemometrics and Intelligent Laboratory Systems, 141(0), 58-67.

Available software

The software used in this paper, can be downloaded from software&downloads