Locality preserving projection （LPP） is a typical and popular dimensionality reduction （DR） method,and it can potentially find discriminative projection directions by preserving the local geometric structure in da...Locality preserving projection （LPP） is a typical and popular dimensionality reduction （DR） method,and it can potentially find discriminative projection directions by preserving the local geometric structure in data. However,LPP is based on the neighborhood graph artificially constructed from the original data,and the performance of LPP relies on how well the nearest neighbor criterion work in the original space. To address this issue,a novel DR algorithm,called the self-dependent LPP （sdLPP） is proposed. And it is based on the fact that the nearest neighbor criterion usually achieves better performance in LPP transformed space than that in the original space. Firstly,LPP is performed based on the typical neighborhood graph; then,a new neighborhood graph is constructed in LPP transformed space and repeats LPP. Furthermore,a new criterion,called the improved Laplacian score,is developed as an empirical reference for the discriminative power and the iterative termination. Finally,the feasibility and the effectiveness of the method are verified by several publicly available UCI and face data sets with promising results.展开更多
When a group of mobile agents track a target,they can locate themselves and the target in a cooperative manner.To maximize the group advantage,a parallel integration strategy of cooperative target-localization(CTL)and...When a group of mobile agents track a target,they can locate themselves and the target in a cooperative manner.To maximize the group advantage,a parallel integration strategy of cooperative target-localization(CTL)and cooperative self-localization(CSL)is designed.Firstly,a global cost function containing the agents’positions and the target’s position is established.Secondly,along with the agents’positions being re-estimated during CTL,the Utransform is employed to propagate the error covariance of the position estimations among the agents.The simulation results show that,the proposal exploits more information for locating the target and the agents than the cases where CTL and CSL run separately,and the global optimal position estimations of the agents and the target are obtained.展开更多
基金Supported by the National Natural Science Foundation of China (60973097)the Scientific Research Foundation of Liaocheng University(X0810029)~~
文摘Locality preserving projection （LPP） is a typical and popular dimensionality reduction （DR） method,and it can potentially find discriminative projection directions by preserving the local geometric structure in data. However,LPP is based on the neighborhood graph artificially constructed from the original data,and the performance of LPP relies on how well the nearest neighbor criterion work in the original space. To address this issue,a novel DR algorithm,called the self-dependent LPP （sdLPP） is proposed. And it is based on the fact that the nearest neighbor criterion usually achieves better performance in LPP transformed space than that in the original space. Firstly,LPP is performed based on the typical neighborhood graph; then,a new neighborhood graph is constructed in LPP transformed space and repeats LPP. Furthermore,a new criterion,called the improved Laplacian score,is developed as an empirical reference for the discriminative power and the iterative termination. Finally,the feasibility and the effectiveness of the method are verified by several publicly available UCI and face data sets with promising results.
文摘When a group of mobile agents track a target,they can locate themselves and the target in a cooperative manner.To maximize the group advantage,a parallel integration strategy of cooperative target-localization(CTL)and cooperative self-localization(CSL)is designed.Firstly,a global cost function containing the agents’positions and the target’s position is established.Secondly,along with the agents’positions being re-estimated during CTL,the Utransform is employed to propagate the error covariance of the position estimations among the agents.The simulation results show that,the proposal exploits more information for locating the target and the agents than the cases where CTL and CSL run separately,and the global optimal position estimations of the agents and the target are obtained.
