The importance of complexity in model selection
Journal of Mathematical Psychology
Key concepts in model selection: performance and generalizability
Journal of Mathematical Psychology
Principal Manifolds for Data Visualization and Dimension Reduction
Principal Manifolds for Data Visualization and Dimension Reduction
Choosing the best model: Level of detail, complexity, and model performance
Mathematical and Computer Modelling: An International Journal
Hi-index | 0.00 |
There are many methods developed to approximate a cloud of vectors embedded in high-dimensional space by simpler objects: starting from principal points and linear manifolds to self-organizing maps, neural gas, elastic maps, various types of principal curves and principal trees, and so on. For each type of approximators the measure of the approximator complexity was developed too. These measures are necessary to find the balance between accuracy and complexity and to define the optimal approximations of a given type. We propose a measure of complexity (geometrical complexity) which is applicable to approximators of several types and which allows comparing data approximations of different types.