This tutorial article reviews models and associated unsupervised learning algorithms for tensor decompositions (TD) Multi-Way Component Analysis (MWCA).Our aim is to make the area of approachable a wider signal processing readership, show how they can be made efficient by incorporating various physically meaningful criteria, constraints assumptions.We next briefly overview emerging approaches multi-block constrained matrix/tensor in applications group-and linkedmultiway component analysis,...

In this paper, we present a multimodal emotion recognition framework called EmotionMeter that combines brain waves and eye movements. To increase the feasibility wearability of in real-world applications, design six-electrode placement above ears to collect electroencephalography (EEG) signals. We combine EEG movements for integrating internal cognitive states external subconscious behaviors users improve accuracy EmotionMeter. The experimental results demonstrate modality fusion with deep...

Nonnegative matrix factorization (NMF) and its extensions such as Tensor Factorization (NTF) have become prominent techniques for blind sources separation (BSS), analysis of image databases, data mining other information retrieval clustering applications. In this paper we propose a family efficient algorithms NMF/NTF, well sparse nonnegative coding representation, that has many potential applications in computational neuroscience, multi-sensory processing, compressed sensing multidimensional...

CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful technique for completion through explicitly capturing the multilinear latent factors. The existing CP algorithms require rank to be manually specified, however, determination remains challenging problem especially . In addition, approaches do not take into account uncertainty information factors, as well missing entries. To address these issues, we formulate using hierarchical probabilistic model and employ fully...

Learning algorithms and underlying basic mathematical ideas are presented for the problem of adaptive blind signal processing, especially instantaneous separation multichannel deconvolution/equalization independent source signals. We discuss developments learning based on natural gradient approach their properties concerning convergence, stability, efficiency. Several promising schemas proposed reviewed in paper. Emphasis is given to neural networks or filtering models associated online...

In this paper, we extend and overview wide families of Alpha-, Beta- Gamma-divergences discuss their fundamental properties. literature usually only one single asymmetric (Alpha, Beta or Gamma) divergence is considered. We show in paper that there exist such divergences with the same consistent Moreover, establish links correspondences among these by applying suitable nonlinear transformations. For example, can generate Beta-divergences directly from Alpha-divergences vice versa....

Machine learning and data mining algorithms are becoming increasingly important in analyzing large volume, multi-relational multi--modal datasets, which often conveniently represented as multiway arrays or tensors. It is therefore timely valuable for the multidisciplinary research community to review tensor decompositions networks emerging tools large-scale analysis mining. We provide mathematical graphical representations interpretation of networks, with main focus on Tucker Tensor Train...

This paper discusses underdetermined (i.e., with more sources than sensors) blind source separation (BSS) using a two-stage sparse representation approach. The first challenging task of this approach is to estimate precisely the unknown mixing matrix. In paper, an algorithm for estimating matrix that can be viewed as extension DUET and TIFROM methods developed. Standard clustering algorithms (e.g., K-means method) also used if are sufficiently sparse. Compared DUET, methods, standard...

Common spatial pattern (CSP)-based filtering has been most popularly applied to electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain-computer interface (BCI) application. The effectiveness of CSP is highly affected by the frequency band and time window EEG segments. Although numerous algorithms have designed optimize spectral bands CSP, them selected a heuristic way. This likely result suboptimal since period when brain responses mental tasks occurs...

Tensor networks have in recent years emerged as the powerful tools for solving large-scale optimization problems. One of most popular tensor network is train (TT) decomposition that acts building blocks complicated networks. However, TT highly depends on permutations dimensions, due to its strictly sequential multilinear products over latent cores, which leads difficulties finding optimal representation. In this paper, we introduce a fundamental model represent large dimensional by circular...

Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs). Despite its efficiency, a problem is that using pre-constructed sine-cosine waves as required reference signals CCA method often does not result optimal accuracy due to their lack features from real electro-encephalo-gram (EEG) data. To address this problem, study proposes novel based on multiset...

Regularization has been one of the most popular approaches to prevent overfitting in electroencephalogram (EEG) classification brain-computer interfaces (BCIs). The effectiveness regularization is often highly dependent on selection parameters that are typically determined by cross-validation (CV). However, CV imposes two main limitations BCIs: 1) a large amount training data required from user and 2) it takes relatively long time calibrate classifier. These substantially deteriorate...

Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and designed reference signals of sine-cosine waves usually works well for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface (BCI) application. However, using the sine- cosine without subject-specific inter-trial information can hardly give optimal accuracy, due to possible overfitting, especially within a short time window length. This paper introduces an L1-regularized...