arXiv research

Paper optimizes clustering for multi-layer networks and discrete mixtures.

Paper perfect clusters sparse, diverse multilayer networks.

Study quantifies performance gap between tensor and matrix-based approaches in nested matrix-tensor model.

A new method clusters data from multiple sources using a mixture of multilayer SBMs.

Tensor-based method simplifies causal skeleton discovery.

In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other. Although several multi-view clustering methods have been proposed, most of them routinely assume one weight for one view of features, and thus inter-vie…

We introduce a tensor-based clustering method to extract sparse, low-dimensional structure from high-dimensional, multi-indexed datasets. This framework is designed to enable detection of clusters of data in the presence of structural requirements which we encode as algebraic constraints in a linear program. Our cluste…

We theoretically and experimentally investigate tensor-based regression and classification. Our focus is regularization with various tensor norms, including the overlapped trace norm, the latent trace norm, and the scaled latent trace norm. We first give dual optimization methods using the alternating direction method …

Tensor Regression tackles high-dimensional data analysis.

Unified approach for clustering financial multiplex networks.

Tensor-EM method learns MoLDS from complex, noisy data.

Paper proposes a tensor model for clustering noisy multi-view data.

A substantial progress in development of new and efficient tensor factorization techniques has led to an extensive research of their applicability in recommender systems field. Tensor-based recommender models push the boundaries of traditional collaborative filtering techniques by taking into account a multifaceted nat…

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

This work introduces a tensor-based method to perform supervised classification on spatiotemporal data processed in an echo state network. Typically when performing supervised classification tasks on data processed in an echo state network, the entire collection of hidden layer node states from the training dataset is …

TSCD is an algorithm for causal discovery using second-order statistics.

The interest in machine learning with tensor networks has been growing rapidly in recent years. We show that tensor-based methods developed for learning the governing equations of dynamical systems from data can, in the same way, be used for supervised learning problems and propose two novel approaches for image classi…

Proposes a novel tensor-based approach for multi-level link prediction.

Structural health monitoring is a condition-based field of study utilised to monitor infrastructure, via sensing systems. It is therefore used in the field of aerospace engineering to assist in monitoring the health of aerospace structures. A difficulty however is that in structural health monitoring the data input is …

The data in many disciplines such as social networks, web analysis, etc. is link-based, and the link structure can be exploited for many different data mining tasks. In this paper, we consider the problem of temporal link prediction: Given link data for times 1 through T, can we predict the links at time T+1? If our da…

Paper introduces an unsupervised tensor-based anomaly detection method for spatiotemporal data.

Paper proposes tensor-based method for semiconductor manufacturing process control.

In this paper we propose a tensor-based nonlinear model for high-order data classification. The advantages of the proposed scheme are that (i) it significantly reduces the number of weight parameters, and hence of required training samples, and (ii) it retains the spatial structure of the input samples. The proposed mo…

Existing imitation learning approaches often require that the complete demonstration data, including sequences of actions and states, are available. In this paper, we consider a more realistic and difficult scenario where a reinforcement learning agent only has access to the state sequences of an expert, while the expe…

Tensor neural network improves human pose classification from 3D skeleton data.

Informed by recent work on tensor singular value decomposition and circulant algebra matrices, this paper presents a new theoretical bridge that unifies the hypercomplex and tensor-based approaches to singular value decomposition and robust principal component analysis. We begin our work by extending the principal comp…

A new tensor-based layer reduces neural network dimensions without losing important features.

TensorShield defends images from adversarial attacks using tensor decomposition.

Rank-R FNN handles high-dimensional data efficiently.

The paper introduces a tensor-based approach to improve neural models' aggregation of structural context.

E$^2$M optimizes tensor density estimation by relaxing $α$-divergence to KL-divergence.

Sparse Tucker decomposition with graph regularization improves time series forecasting accuracy.

The tensor-tensor product (t-product) [M. E. Kilmer and C. D. Martin, 2011] is a natural generalization of matrix multiplication. Based on t-product, many operations on matrix can be extended to tensor cases, including tensor SVD, tensor spectral norm, tensor nuclear norm [C. Lu, et al., 2018] and many others. The line…

Scorio.jl ranks systems from repeated tasks using various methods.

Deep learning models predict option prices from 3D tensor data.

Machine learning models accurately predict molecular magnetic anisotropy tensors.

In this work, we consider Corporate Governance (CG) ties among companies from a multiple network perspective. Such a structure naturally arises from the close interrelation between the Shareholding Network (SH) and the Board of Directors network (BD). In order to capture the simultaneous effects of both networks on CG,…

Money analyzed as a multidimensional tensor for better economic policy.

Paper proposes a new tensor imputation method for spatiotemporal traffic data with missing patterns.

Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem. In this paper we propose a novel spectral approach to this problem, based on the eigenvectors of both the second order moment matrix and t…

TRNN combines tensor geometry with neural network nonlinearity for HD data.

Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and …

Forecasting based on financial time-series is a challenging task since most real-world data exhibits nonstationary property and nonlinear dependencies. In addition, different data modalities often embed different nonlinear relationships which are difficult to capture by human-designed models. To tackle the supervised l…

A new algorithm speeds up CP decomposition for large tensors.

We propose a tensor-based model that fuses a more granular representation of user preferences with the ability to take additional side information into account. The model relies on the concept of ordinal nature of utility, which better corresponds to actual user perception. In addition to that, unlike the majority of h…

New model mimics neural next item recommendation using Hankel matrices.

Recent years have seen rapid advances in the data-driven analysis of dynamical systems based on Koopman operator theory and related approaches. On the other hand, low-rank tensor product approximations -- in particular the tensor train (TT) format -- have become a valuable tool for the solution of large-scale problems …

In this paper we propose new techniques to sample arbitrary third-order tensors, with an objective of speeding up tensor algorithms that have recently gained popularity in machine learning. Our main contribution is a new way to select, in a biased random way, only $O(n^{1.5}/ε^2)$ of the possible $n^3$ elements while s…