On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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5 results for “HOOI”

A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iterationmath.ST

Novel tensor perturbation bounds for orthogonal iteration methods.

problem Developing robust bounds for tensor reconstruction and subspace estimation.

method Blockwise tensor perturbation bounds for high-order orthogonal iteration (HOOI).

result Upper bounds for singular subspace estimation converge linearly and tensor reconstruction error bound is characterized by a simple quantity.

tensor perturbation orthogonal iteration singular subspace estimation HOOI tensor reconstruction

2020-08-06abs ↗pdf ↗

Estimating Higher-Order Mixed Memberships via the

\ell_{2,\infty}

Tensor Perturbation Boundmath.ST

Paper proposes a new tensor model for mixed memberships and provides error bounds.

problem Estimating mixed memberships in higher-order multiway data.

method Tensor mixed-membership blockmodel, higher-order orthogonal iteration algorithm (HOOI), simplex corner-finding algorithm.

result Consistency of estimation procedure with error bounds under specific conditions.

tensor mixed memberships higher-order multiway data convex combinations HOOI algorithm error bounds

2022-12-16abs ↗pdf ↗

A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximationstat.ML

Study uses random matrix theory to improve tensor approximation accuracy.

problem Improving tensor approximation accuracy in the presence of noise.

method Random matrix theory applied to tensor unfoldings.

result Characterizes spectral behavior of tensor unfoldings and predicts reconstruction performance.

Random Matrix Theory Tensor Approximation Low-Rank Signal Spectral Behavior Multilinear SVD

2024-02-05abs ↗pdf ↗

Oblivious subspace embeddings for compressed Tucker decompositionsstat.ML

This work improves tensor decomposition methods, especially for large datasets.

problem Lack of efficient methods for estimating Tucker decompositions.

method Applies Johnson-Lindenstrauss type guarantees to Tucker decompositions with random embeddings.

result Effective dimension reduction with minimal error for large tensors.

tensor decomposition Johnson-Lindenstrauss Tucker decomposition random embeddings image processing

2024-06-13abs ↗pdf ↗

The Exact Solution to Rank-1 L1-norm TUCKER2 Decompositioncs.DS

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. Our contributions are as follows. i) We prove that the problem is equivalent to combinatorial optimization over $N$ antipodal-binary variables. ii)…

2017-10-31abs ↗pdf ↗