arXiv research

A new algorithm speeds up CP decomposition for large tensors.

New method models matrix time series using tensor CP-decomposition.

Constructs a simplicial cell decomposition of complex projective space for n ≥ 2.

Revisits CP tensor decomposition for noisy, non-orthogonal data.

New algorithms improve tensor CP decomposition under mild conditions.

The problem of Knowledge Base Completion can be framed as a 3rd-order binary tensor completion problem. In this light, the Canonical Tensor Decomposition (CP) (Hitchcock, 1927) seems like a natural solution; however, current implementations of CP on standard Knowledge Base Completion benchmarks are lagging behind their…

Develops SymGCP for tensor decompositions with general symmetry.

We give new rational blowdown constructions of exotic CP^2#n(-CP^2) (5\leq n\leq 9) without using elliptic fibrations. We also show that our 4-manifolds admit handle decompositions without 1- and 3-handles, for 7\leq n\leq 9. A strategy for rational blowdown constructions of exotic CP^2#n(-CP^2) (1\leq n\leq 4) is also…

Unified algorithm for tensor decomposition supports multiple loss functions and models.

TGCCA analyzes higher-order tensors using orthogonal rank-R CP decomposition.

New complex structure on hyperbolic disc within hyperkaehler space.

Two methods preserve tensor structure for reduced dimensionality in tensor regression.

Methods based on vector embeddings of knowledge graphs have been actively pursued as a promising approach to knowledge graph completion.However, embedding models generate storage-inefficient representations, particularly when the number of entities and relations, and the dimensionality of the real-valued embedding vect…

Classifies 4-manifolds with genus one horizontal handlebody decomposition.

Efficient tensor decomposition for count data models achieves near-optimal multiway analysis.

We consider the problem of online subspace tracking of a partially observed high-dimensional data stream corrupted by noise, where we assume that the data lie in a low-dimensional linear subspace. This problem is cast as an online low-rank tensor completion problem. We propose a novel online tensor subspace tracking al…

Smooth 4-manifolds have simple horizontal decompositions.

Tensor decompositions are powerful tools for large data analytics as they jointly model multiple aspects of data into one framework and enable the discovery of the latent structures and higher-order correlations within the data. One of the most widely studied and used decompositions, especially in data mining and machi…

New 4-manifolds found without 1- and 3-handles.

Tensorized random projections reduce high-dimensional tensor size efficiently.

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

CP-factorization for high-dimensional tensor time series and double projection iterations

Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in statistical learning of latent variable models and in data mining. In this paper, we propose fast and randomized tensor CP decomposition algorithms based on sketching. We build on the idea of count sketches, but introduce many novel ideas which are un…

In this paper, we study a polynomial decomposition model that arises in problems of system identification, signal processing and machine learning. We show that this decomposition is a special case of the X-rank decomposition --- a powerful novel concept in algebraic geometry that generalizes the tensor CP decomposition…

We construct instanton Floer homology for lens spaces $L(p,q)$. As an application, we prove that $X = \CP^2 # \CP^2$ does not admit a decomposition $X = X_1 \cup X_2$. Here $X_1$ and $X_2$ are oriented, simply connected, non-spin 4-manifolds with $b^+ = 1$ and with boundary $L(p, 2)$, and $p$ is a prime number of the f…

Quantization on even-dimensional compact manifolds using cell decomposition.

New LSH methods for tensor data improve efficiency and space usage.

Robust tensor CP decomposition involves decomposing a tensor into low rank and sparse components. We propose a novel non-convex iterative algorithm with guaranteed recovery. It alternates between low-rank CP decomposition through gradient ascent (a variant of the tensor power method), and hard thresholding of the resid…

High-dimensional tensors or multi-way data are becoming prevalent in areas such as biomedical imaging, chemometrics, networking and bibliometrics. Traditional approaches to finding lower dimensional representations of tensor data include flattening the data and applying matrix factorizations such as principal component…

We propose an extension of the canonical polyadic (CP) tensor model where one of the latent factors is allowed to vary through data slices in a constrained way. The components of the latent factors, which we want to retrieve from data, can vary from one slice to another up to a diffeomorphism. We suppose that the diffe…

The paper embeds 4-manifolds into CP^2 x CP^1 using Lefschetz fibrations.

In this paper, we first prove that any closed simply connected 4-manifold that admits a decomposition into two disk bundles of rank greater than 1 is diffeomorphic to one of the standard elliptic 4-manifolds: $\mathbb{S}^4$, $\mathbb{CP}^2$, $\mathbb{S}^2\times\mathbb{S}^2$, or $\mathbb{CP}^2\#\pm \mathbb{CP}^2$. As an…

New method solves elliptic equations on manifolds without grids.

New ADMM method for PARAFAC2 tensor decomposition with flexible regularization.

Matrix factorizations and their extensions to tensor factorizations and decompositions have become prominent techniques for linear and multilinear blind source separation (BSS), especially multiway Independent Component Analysis (ICA), NonnegativeMatrix and Tensor Factorization (NMF/NTF), Smooth Component Analysis (Smo…

Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs). Although many decompositions are conceivable, only CP decomposition and a few others have been applied in practice, and no extensive comparisons have been made between available methods. Previo…

4-manifolds can be broken down into pairs-of-pants and K3 surfaces.

Let S be a surface with genus g and n boundary components and let d(S) = 3g-3+n denote the number of curves in any pants decomposition of S. We employ metric properties of the graph of pants decompositions CP(S) prove that the Weil-Petersson metric on Teichmuller space Teich(S) is Gromov-hyperbolic if and only if d(S) …

New method compresses deep learning layers using tensor decomposition.

NeCPD improves online tensor decomposition using SGD with Hessian analysis and NAG.

New algorithm for online tensor factorization with provable guarantees.

Paper tackles tensor decomposition for unaligned observations using RKHS and novel loss functions.

Recovery of low-rank matrices from a small number of linear measurements is now well-known to be possible under various model assumptions on the measurements. Such results demonstrate robustness and are backed with provable theoretical guarantees. However, extensions to tensor recovery have only recently began to be st…

New algorithm learns interpretable CP-basis from streaming tensor data under Markovian constraints.

NeSGD efficiently updates tensor-based features for online model learning in multi-way data.

Using tropical geometry, Mikhalkin has proved that every smooth complex hypersurface in $\mathbb{CP}^{n+1}$ decomposes into pairs of pants: a pair of pants is a real compact $2n$-manifold with cornered boundary obtained by removing an open regular neighborhood of $n+2$ generic hyperplanes from $\mathbb{CP}^n$. As is we…

A new framework improves tensor completion accuracy by considering numerical priors.

Tensor factorization has become an increasingly popular approach to knowledge graph completion(KGC), which is the task of automatically predicting missing facts in a knowledge graph. However, even with a simple model like CANDECOMP/PARAFAC(CP) tensor decomposition, KGC on existing knowledge graphs is impractical in res…