# Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning

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

# Robust Singular Values based on L1-norm PCA

no code implementations21 Oct 2022,

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

# Minimum Mean-Squared-Error Autocorrelation Processing in Coprime Arrays

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

# Structured Autocorrelation Matrix Estimation for Coprime Arrays

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.

# The Exact Solution to Rank-1 L1-norm TUCKER2 Decomposition

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.

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# Efficient L1-Norm Principal-Component Analysis via Bit Flipping

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].

# Some Options for L1-Subspace Signal Processing

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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