Enhanced Online Subspace Estimation via Adaptive Sensing
نویسندگان
چکیده
This work investigates the problem of adaptive measurement design for online subspace estimation from compressive linear measurements. We study the previously proposed Grassmannian rank-one online subspace estimation (GROUSE) algorithm with adaptively designed compressive measurements. We propose an adaptive measurement scheme that biases the measurement vectors towards the current subspace estimate and prove a global convergence result for the resulting algorithm. Our experiments on synthetic data demonstrate the effectiveness of the adaptive measurement scheme over non-adaptive compressive random measurements.
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