arXiv research

The paper tackles tensor factorization and completion from noisy data.

Proposes a method for tensor completion with sparse factors and missing data.

NNEinFact fits any nonnegative tensor factorization quickly and accurately.

Paper studies nonnegative Tucker decomposition identifiability with sparsity conditions.

Efficiently factorizes coupled matrix tensor data for better accuracy and speed.

Efficient NTF algorithm for large sparse tensors.

We present a novel nonnegative tensor decomposition method, called Legendre decomposition, which factorizes an input tensor into a multiplicative combination of parameters. Thanks to the well-developed theory of information geometry, the reconstructed tensor is unique and always minimizes the KL divergence from an inpu…

New hierarchical tensor decomposition model for complex data.

Proposes CC-NMDF for analyzing manifold-valued data.

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

This paper is concerned with improving the empirical convergence speed of block-coordinate descent algorithms for approximate nonnegative tensor factorization (NTF). We propose an extrapolation strategy in-between block updates, referred to as heuristic extrapolation with restarts (HER). HER significantly accelerates t…

There is currently an unprecedented demand for large-scale temporal data analysis due to the explosive growth of data. Dynamic topic modeling has been widely used in social and data sciences with the goal of learning latent topics that emerge, evolve, and fade over time. Previous work on dynamic topic modeling primaril…

The paper improves density estimation in high dimensions using tensor decompositions.

We augment the nonnegative matrix factorization method for audio source separation with cues about directionality of sound propagation. This improves separation quality greatly and removes the need for training data, with only a twofold increase in run time. This is the first method which can exploit directional inform…

It has been recently shown that sparse, nonnegative tensor factorization of multi-modal electronic health record data is a promising approach to high-throughput computational phenotyping. However, such approaches typically do not leverage available domain knowledge while extracting the phenotypes; hence, some of the su…

Study on gradient ρ-Einstein solitons with radially nonnegative Bach tensor.

Introduces nondecreasing rank for matrices and tensors, developing methods and applications.

We propose a completely unsupervised method to understand audio scenes observed with random microphone arrangements by decomposing the scene into its constituent sources and their relative presence in each microphone. To this end, we formulate a neural network architecture that can be interpreted as a nonnegative tenso…

Decomposes submanifolds with special tensors into simpler parts.

Log Sobolev and Michael Simon inequalities for tensor fields on curved manifolds.

New algorithm for nonnegative tensor completion with linear convergence rate.

Enhances tensor regression for interpretability and performance.

Joint analysis of data from multiple information repositories facilitates uncovering the underlying structure in heterogeneous datasets. Single and coupled matrix-tensor factorization (CMTF) has been widely used in this context for imputation-based recommendation from ratings, social network, and other user-item data. …

In this note we classify compact 4-manifolds with harmonic Weyl tensor and nonnegative biorthogonal curvature

Proves inequality for tensor fields on curved spaces.

Alexandrov spaces with non-negative curvature are characterized by the matrix displacement convexity of an entropy tensor.

We introduce a dynamic generative model, Bayesian allocation model (BAM), which establishes explicit connections between nonnegative tensor factorization (NTF), graphical models of discrete probability distributions and their Bayesian extensions, and the topic models such as the latent Dirichlet allocation. BAM is base…

Nonnegative CANDECOMP/PARAFAC (NCP) decomposition is an important tool to process nonnegative tensor. Sometimes, additional sparse regularization is needed to extract meaningful nonnegative and sparse components. Thus, an optimization method for NCP that can impose sparsity efficiently is required. In this paper, we co…

Nonnegative matrix factorization (NMF) has been widely used in machine learning and signal processing because of its non-subtractive, part-based property which enhances interpretability. It is often assumed that the latent dimensionality (or the number of components) is given. Despite the large amount of algorithms des…

In this paper we prove that any complete conformal gradient soliton with nonnegative Ricci tensor is either isometric to a direct product $\mathbb{R}\times N^{n-1}$, or globally conformally equivalent to the Euclidean space $\mathbb{R}^{n}$ or to the round sphere $\mathbb{S}^{n}$. In particular, we show that any comple…

In this letter, we propose a new identification criterion that guarantees the recovery of the low-rank latent factors in the nonnegative matrix factorization (NMF) model, under mild conditions. Specifically, using the proposed criterion, it suffices to identify the latent factors if the rows of one factor are \emph{suf…

Graph neural networks speed up nonnegative matrix factorization.

The exact nonnegative matrix factorization (exact NMF) problem is the following: given an $m$-by-$n$ nonnegative matrix $X$ and a factorization rank $r$, find, if possible, an $m$-by-$r$ nonnegative matrix $W$ and an $r$-by-$n$ nonnegative matrix $H$ such that $X = WH$. In this paper, we propose two heuristics for exac…

An algorithm for computing positive semidefinite factorizations of matrices.

Bayesian NMF model improves predictions and avoids overfitting.

DeepTensor uses deep networks to efficiently decompose tensors with improved performance and robustness.

A new NMF variant tackles underdetermined problems with sparse and separable assumptions.

New NMF algorithm uses Toeplitz matrix for facial recognition.

The paper classifies compact quasi-Einstein manifolds with boundary.

Nonnegative matrix factorization (NMF) is a powerful tool for data mining. However, the emergence of `big data' has severely challenged our ability to compute this fundamental decomposition using deterministic algorithms. This paper presents a randomized hierarchical alternating least squares (HALS) algorithm to comput…

In this paper, we study stable weighted minimal hypersurfaces in manifolds with nonnegative Bakry-Emery Ricci curvature. We will give some geometric and topological applications. In particular, we give some partial classification of complete 3-manifolds with nonnegative Bakry-Emery Ricci curvature assuming that $f$ is …

This work proposes a new method to estimate joint probability from pairwise marginals, reducing sample complexity.

New algorithm completes nonnegative tensors with fewer samples and faster convergence.

This paper classifies solitons under specific tensor conditions.

Nonnegative matrix factorization (NMF), a dimensionality reduction and factor analysis method, is a special case in which factor matrices have low-rank nonnegative constraints. Considering the stochastic learning in NMF, we specifically address the multiplicative update (MU) rule, which is the most popular, but which h…

ZNMF improves facial recognition performance using data-dependent penalties.

Nonnegative Boltzmann machines (NNBMs) are recurrent probabilistic neural network models that can describe multi-modal nonnegative data. NNBMs form rectified Gaussian distributions that appear in biological neural network models, positive matrix factorization, nonnegative matrix factorization, and so on. In this paper,…

We study the problem of nonnegative rank-one approximation of a nonnegative tensor, and show that the globally optimal solution that minimizes the generalized Kullback-Leibler divergence can be efficiently obtained, i.e., it is not NP-hard. This result works for arbitrary nonnegative tensors with an arbitrary number of…