no code implementations • 15 Jan 2024 • Manish Sharma, Jamison Heard, Eli Saber, Panos P. Markopoulos

To address these issues, we propose an efficient training method for CNN compression via dynamic parameter rank pruning.

no code implementations • 21 Oct 2022 • Duc Le, Panos P. Markopoulos

The L2-norm (sum of squared values) formulation of PCA promotes peripheral data points and, thus, makes PCA sensitive against outliers.

no code implementations • 21 Oct 2020 • Dimitris G. Chachlakis, Tongdi Zhou, Fauzia Ahmad, Panos P. Markopoulos

Coprime arrays enable Direction-of-Arrival (DoA) estimation of an increased number of sources.

no code implementations • 27 Aug 2020 • Dimitris G. Chachlakis, Panos P. Markopoulos

A coprime array receiver processes a collection of received-signal snapshots to estimate the autocorrelation matrix of a larger (virtual) uniform linear array, known as coarray.

1 code implementation • 31 Oct 2017 • Panos P. Markopoulos, Dimitris G. Chachlakis, Evangelos E. Papalexakis

We study rank-1 {L1-norm-based TUCKER2} (L1-TUCKER2) decomposition of 3-way tensors, treated as a collection of $N$ $D \times M$ matrices that are to be jointly decomposed.

no code implementations • 6 Oct 2016 • Panos P. Markopoulos, Sandipan Kundu, Shubham Chamadia, Dimitris A. Pados

It was shown recently that the $K$ L1-norm principal components (L1-PCs) of a real-valued data matrix $\mathbf X \in \mathbb R^{D \times N}$ ($N$ data samples of $D$ dimensions) can be exactly calculated with cost $\mathcal{O}(2^{NK})$ or, when advantageous, $\mathcal{O}(N^{dK - K + 1})$ where $d=\mathrm{rank}(\mathbf X)$, $K<d$ [1],[2].

no code implementations • 4 Sep 2013 • Panos P. Markopoulos, George N. Karystinos, Dimitris A. Pados

We describe ways to define and calculate $L_1$-norm signal subspaces which are less sensitive to outlying data than $L_2$-calculated subspaces.

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