Iterative Majorization Approach to the Distance-based Discriminant Analysis

Abstract

This paper proposes a method of finding a discriminative linear transformation that enhances the data's degree of conformance to the compactness hypothesis and its inverse. The problem formulation relies on inter-observation distances only, which is shown to improve non-parametric and non-linear classifier performance on benchmark and real-world data sets. The proposed approach is suitable for both binary and multiple-category classification problems, and can be applied as a dimensionality reduction technique. In the latter case, the number of necessary discriminative dimensions can be determined exactly. The sought transformation is found as a solution to an optimization problem using iterative majorization.

Hierarchical ensemble learning for multimedia categorization and autoannotation

“…boat, sailboat, ferryboat, rowboat, at the expense of precision. Using the DDA baseline classifiers [8,9] for each concept C i ∈ H, the following precision and recall results on the test set vocabulary were obtained (see Figure 3). As seen from the figure, the naturally high recall results boosted by keyword group retrieval, Figure 3(a) do not necessarily correspond to high frequency common concepts emphasizing the importance of the concept co-occurrence factors, while the significantly lower precision values for complex concepts, such as church, fence, boat, Figure 3(b), indicate that these words are much more often retrieved as a group of semanticallyrelated keywords, rather than individually.…”

Visual Object Categorization using Distance-Based Discriminant Analysis

“…In additional to its computational advantages, the majorization method has the valuable properties of low dependence on the initial value [32] and enhanced robustness with respect to local minima problems [20]. In the next section, we will outline a way to derive majorizing expressions of (2) and show how they can be used for optimizing the chosen criterion.…”