5 accepted papers
Standard Tucker tensor decomposition seeks to maximize the L2-norm of the compressed tensor; thus, it is very responsive to outlying/high-magnitude entries among the processed data. To counteract the impact of outliers in tensor data analysis, we propose L1-Tucker: a reformulation of standard Tucker…
Sampling the physical-array autocorrelations is the initial processing step in standard direction-of-arrival (DoA) estimation with coprime arrays. These samples are then organized into an autocorrelation matrix estimate of a uniform-linear virtual coarray, which in turn is used for DoA estimation. E…
We consider corruption-resistant L1-norm-based TUCKER2 (L1-TUCKER2) decomposition of a D×M×N 3-way tensor, treated (with no loss of generality) as a collection of N D×M matrices. Our contributions are as follows. First, we show that rank-1 L1-TUCKER2 can be cast as a combinatorial problem over N ant…
Statistically-optimal Linear Discriminant Analysis (LDA) is formulated as a maximization that involves the nominal statistics of the classes to be discriminated. In practice, however, these nominal statistics are unknown and estimated from a collection of labeled training data. Accordingly, the nomi…
We introduce maximum-SINR sparse-binary waveforms that modulate data information symbols from any finite alphabet and span the whole continuum of the available/device-accessible spectrum. We offer an optimal algorithm that designs the proposed waveforms by maximizing the signal-to-interference-plus-…