Minimax Rank Estimation for Subspace Tracking

Blind channel identification: subspace tracking method without rank estimation

Subspace (SS) methods are an effective approach for blind channel identification. However, thses methods also have two major disadvantages: 1) They require accurate channel length estimation and/or rank estimation of the correlation matrix, which is difficult with noisy channels, and 2) they require a large amount of computation for the singular value decomposition (SVD), which makes it inconve...

Minimax Sparse Principal Subspace Estimation in High Dimensions

We study sparse principal components analysis in high dimensions , where p (the number of variables) can be much larger than n (the number of observations), and analyze the problem of estimating the subspace spanned by the principal eigenvectors of the population covariance matrix. We prove optimal, non-asymptotic lower and upper bounds on the minimax subspace estimation error under two differe...

Subspace-tracking methods for channel estimation in OFDM systems

Supervised channel estimation in OFDM systems typically consists in the interpolation/smoothing over the entire time-frequency grid of the channel gains estimated on the pilot subcarriers. In this process, only statistical informations about the propagation channel are exploited (i.e., Doppler spectrum and power-delay profile). We propose a novel technique that is based on a general parametric ...

Point cloud normal estimation via low-rank subspace clustering

In this paper, we present a robust normal estimation algorithm based on the low-rank subspace clustering technique. The main idea is based on the observation that compared with the points around sharp features, it is relatively easier to obtain accurate normals for the points within smooth regions. The points around sharp features and smooth regions are identified by covariance analysis of thei...

Minimax lower bounds for estimation