Multiple Kernel Dimensionality Reduction via Ratio‐Trace and Marginal Fisher Analysis

Abstract

Traditional supervised multiple kernel learning (MKL) for dimensionality reduction is generally an extension of kernel discriminant analysis (KDA), which has some restrictive assumptions. In addition, they generally are based on graph embedding framework. A more general multiple kernel-based dimensionality reduction algorithm, called multiple kernel marginal Fisher analysis (MKL-MFA), is presented for supervised nonlinear dimensionality reduction combined with ratio-race optimization problem. MKL-MFA aims at relaxing the restrictive assumption that the data of each class is of a Gaussian distribution and finding an appropriate convex combination of several base kernels. To improve the efficiency of multiple kernel dimensionality reduction, the spectral regression frameworks are incorporated into the optimization model. Furthermore, the optimal weights of predefined base kernels can be obtained by solving a different convex optimization. Experimental results on benchmark datasets demonstrate that MKL-MFA outperforms the state-of-the-art supervised multiple kernel dimensionality reduction methods.

The energy-efficient MDA-SMAC protocol for wireless sensor networks

“…In high network traffic, compared to the S-MAC protocol, due to the MDA-SMAC protocol, using an adaptive duty cycle and adaptive back-off algorithm can reduce the impact of conflict on data transmission, so data latency is less than S-MAC protocol. With the increase of contract intervals, in both reducing delays, delays in MDA-SAMC agreement are still less than the S-MAC protocol [24][25][26].…”