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Feature selection aims to gain relevant features for improved classification performance and remove redundant features for reduced computational cost. How to balance these two factors is a problem especially when the categorical labels are costly to obtain. In this paper, we address this problem using semisupervised learning method and propose a max-relevance and min-redundancy criterion based on Pearson's correlation (RRPC) coefficient. This new method uses the incremental search technique to select optimal feature subsets. The new selected features have strong relevance to the labels in supervised manner, and avoid redundancy to the selected feature subsets under unsupervised constraints. Comparative studies are performed on binary data and multicategory data from benchmark data sets. The results show that the RRPC can achieve a good balance between relevance and redundancy in semisupervised feature selection. We also compare the RRPC with classic supervised feature selection criteria (such as mRMR and Fisher score), unsupervised feature selection criteria (such as Laplacian score), and semisupervised feature selection criteria (such as sSelect and locality sensitive). Experimental results demonstrate the effectiveness of our method.

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Secure wireless communications is a challenging problem due to the shared nature of the wireless medium. Most existing security mechanisms focus on traditional cryptographic schemes. In recent years, features of the multi-path channel (such as randomness and reciprocity), have driven researchers to exploit its potential to enhance the security of wireless networks. As OFDM occupies wide bandwidth, it will experience a prolific source of multi-path components. In this paper, we comprehensively exploit the inherent physical parameters of the multi-path fading channel to achieve continuous two way authentication between wireless terminals. In our scheme, pilot information is randomly spread in a wideband channel, leading to low probability of detection (LPD). Unlike other channel-based approaches, the information of both amplitude and phase in the channel signature is fully utilized to enhance the security of the OFDM communication network. More specially, the receiver will detect the channel response continuously according to the randomly inserted pilots and identify the valid user based on the statistical channel signature information. Simulation results indicate the high efficiency of the proposed method.