Iterative application of dimension reduction methods

Application-Driven Dimension Reduction

Simplicity and efficiency of linear transformations make them a popular tool for reducing dimensions (of data) before or during statistical analysis. Examples of their applications include image compression and reconstruction, data clustering, pattern classification, and image or text retrieval. Linear transformations with natural orthogonality constraints can be represented as elements of Stie...

Application of Iterative Methods for Solving General Riccati Equation

Analysis of Correlation Based Dimension Reduction Methods

Dimension reduction is an important topic in data mining and machine learning. Especially dimension reduction combined with feature fusion is an effective preprocessing step when the data are described by multiple feature sets. Canonical Correlation Analysis (CCA) and Discriminative Canonical Correlation Analysis (DCCA) are feature fusion methods based on correlation. However, they are differen...

Subset selection in dimension reduction methods

Dimension reduction methods play an important role in multivariate statistical analysis, in particular with high-dimensional data. Linear methods can be seen as a linear mapping from the original feature space to a dimension reduction subspace. The aim is to transform the data so that the essential structure is more easily understood. However, highly correlated variables provide redundant infor...

Dimension Reduction Methods for Iris Recognition

In this paper, we compare performance of several dimension reduction techniques, namely LSI, FastMap, and SDD in Iris recognition. We compare the quality of these methods from both the visual impact, and quality of generated "eigenirises".