Search Results for author:
Found 7 papers, 1 papers with code
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
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.
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.
