SPS Feed

Principal component analysis is one of the most commonly used methods for dimensionality reduction in signal processing. However, the most commonly used PCA formulation is based on the L2 -norm, which can be highly influenced by outlier data. In recent years, there has been growing interest in the development of more robust PCA methods. 

We study model recovery for data classification, where the training labels are generated from a one-hidden-layer neural network with sigmoid activations, also known as a single-layer feedforward network, and the goal is to recover the weights of the neural network. We consider two network models, the fully-connected network (FCN) and the non-overlapping convolutional neural network (CNN).

The localizability analysis for wireless sensor network is of great significance to network localization, and topology control. In this paper, the localizability problem for the bearing-based localization is investigated. An identification method for bearing rigid component is presented, and the localizability is studied for the determined bearing rigid component. In the identification process for bearing rigid component, the center node is introduced, and an approach for identifying the bearing rigid component is proposed based on the characteristic of the bearing rigid graph by using the center nodes.