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We derive lower- and upper-bounds of α-divergence between univariate mixture models with components in the exponential family. Three pairs of bounds are presented in order with increasing quality and increasing computational cost. They are verified empirically through simulated Gaussian mixture models. The presented methodology generalizes to other divergence families relying on Hellinger-type integrals.
This paper presents a robust velocity estimation algorithm applicable to low-cost monocular platform with a tri-axial gyroscope. The algorithm is developed based on PX4FLOW platform which is an open software and open hardware solution for monocular speed estimation. The paper provides three main contributions. The first is the improved robustness achieved by various methods including feature block...
The representative samples can be pictured as the skeleton of a point cloud. We learn a discrete distribution defined over all samples, so that these skeleton points have large probabilities and the outliers have probabilities close to zero. The basic assumption is that any observation is generated from a nearby skeleton point. The learning objective is to minimize the communication cost from a random...
A novel STAP algorithm based on sparse recovery technique, called CS-STAP, were presented. Instead of using conventional maximum likelihood estimation of covariance matrix, our method utilizes the echo statistics on spatial-temporal plane, which is extracted from sample data of only ONE training range cell with Compressed Sensing techniques, to construct a new estimator of covariance matrix, and build...
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