Hierarchical Discriminant Analysis

Hierarchical Discriminant Analysis

Blog Article

The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data.The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional Embroidery Notions data, and abandon the least useful information in the subsequent processing.

In this context, many subspace learning algorithms have been presented.However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem.That means that the impact of intra-class distance is overwhelmed.To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA).

It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance.This proposed method balances the bias from the inter-class and that Pot Supports from the intra-class to achieve better performance.Extensive experiments are conducted on several benchmark face datasets.The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.