Visualization

Course Code : IA346

Time Hours : 18 hours

Time Periods : not available yet

Lecturers :

Objective

The purpose of this course is to describe the visualization techniques linear and non linear commonly used in different fields such as bioinformatics, text mining, web mining, image and speech processing.

Acquired Skills

In which context we can use each method and how the results can be interpreted on real situations while taking into account the size and the dimension of data.

Contents

  •  Dimension reduction
  •  Principal component analysis
  •  Correspondence analysis
  •  Multiple correspondence cnalysis
  •  Factor analysis
  •  Independant component analysis
  •  Multidimensional Scaling and approximation
  •  Manifold Learning : Isomap, LLE

References

  •  Data Analysis, Gérad Govaert, ISTE Ltd and John Wiley & Sons Inc (2009)
  •  Nonlinear Dimensionality Reduction John A. Lee and Michel Verleysen, (Information Science and Statistics), (2010)
  •  Principal Manifolds for Data Visualization and Dimension Reduction (Lecture Notes in Computational Science and Engineering), Alexander N. Gorban, Balázs Kégl , Donald C. Wunsch and Andrei Zinovyev (Editor) Springer ; 1 edition (October 24, 2007)