This paper presents a novel non-negative tensor factorization (NTF) based approach to tracking and separation of moving sound sources, formulated in the Spherical Harmonic Domain (SHD). In particular, at first, we red...
详细信息
This paper presents a novel non-negative tensor factorization (NTF) based approach to tracking and separation of moving sound sources, formulated in the Spherical Harmonic Domain (SHD). In particular, at first, we redefine an already existing Ambisonic NTF by introducing time-dependence into the Spatial Covariance Matrix (SCM) model. Next, we further extend the time-dependent SCM by incorporating a newly proposed NTF model of the spatial features, thereby introducing spatial components. To exploit the relationship between the positions of sound sources in adjacent time frames, resulting from the naturally occurring continuity of the movement itself, we impose local smoothness on time-dependent components of the spatial features. To this end, we propose a suitable posterior probability with Gibbs prior, and finally we derive the corresponding update rules. The experimental evaluation is based on first-order Ambisonic recordings of speech utterances and musical instruments in several scenarios with moving sources.
Many image completion methods are based on a low-rank approximation of the underlying image using matrix or tensor decomposition models. In this study, we assume that the image to be completed is represented by a mult...
详细信息
Many image completion methods are based on a low-rank approximation of the underlying image using matrix or tensor decomposition models. In this study, we assume that the image to be completed is represented by a multi-way array and can be approximated by a conical hull of subtensors in the observation space. If an observed tensor is near-separable along at least one mode, the extreme rays, represented by the selected subtensors, can be found by analyzing the corresponding convex hull. Following this assumption, we propose a geometric algorithm to address a low-rank image completion problem. The extreme rays are extracted with a segmented convex-hull algorithm that is suitable for performing noise-resistant non-negative tensor factorization. The coefficients of a conical combination of such rays are estimated using Douglas-Rachford splitting combined with the rank-two update least-squares algorithm. The proposed algorithm was applied to incomplete RGB images and a hyperspectral 3D array with a large number of randomly missing entries. Experiments confirm its good performance with respect to other well-known image completion methods.
Recently, non-negative tensor factorization (NTF) as a very powerful tool has attracted the attention of researchers. It is used in the unmixing of hyperspectral images (HSI) due to its excellent expression ability wi...
详细信息
Recently, non-negative tensor factorization (NTF) as a very powerful tool has attracted the attention of researchers. It is used in the unmixing of hyperspectral images (HSI) due to its excellent expression ability without any information loss when describing data. However, most of the existing unmixing methods based on NTF fail to fully explore the unique properties of data, for example, low rank, that exists in both the spectral and spatial domains. To explore this low-rank structure, in this paper we learn the different low-rank representations of HSI in the spectral, spatial and non-local similarity modes. Firstly, HSI is divided into many patches, and these patches are clustered multiple groups according to the similarity. Each similarity group can constitute a 4-D tensor, including two spatial modes, a spectral mode and a non-local similarity mode, which has strong low-rank properties. Secondly, a low-rank regularization with logarithmic function is designed and embedded in the NTF framework, which simulates the spatial, spectral and non-local similarity modes of these 4-D tensors. In addition, the sparsity of the abundance tensor is also integrated into the unmixing framework to improve the unmixing performance through the L2,1 norm. Experiments on three real data sets illustrate the stability and effectiveness of our algorithm compared with five state-of-the-art methods.
non-negative tensor factorization (NTF) has been successfully applied to analyze event-related potentials (ERPs), and shown superiority in terms of capturing multi-domain features. However, the time-frequency represen...
详细信息
non-negative tensor factorization (NTF) has been successfully applied to analyze event-related potentials (ERPs), and shown superiority in terms of capturing multi-domain features. However, the time-frequency representation of ERPs by higher-order tensors are usually large-scale, which prevents the popularity of most tensorfactorization algorithms. To overcome this issue, we introduce a non-negative canonical polyadic decomposition (NCPD) based on low-rank approximation (LRA) and hierarchical alternating least square (HALS) techniques. We applied NCPD (LRAHALS and benchmark HALS) and CPD to extract multi-domain features of a visual ERP. The features and components extracted by LRAHALS NCPD and HALS NCPD were very similar, but LRAHALS NCPD was 70 times faster than HALS NCPD. Moreover, the desired multi-domain feature of the ERP by NCPD showed a significant group difference (control versus depressed participants) and a difference in emotion processing (fearful versus happy faces). This was more satisfactory than that by CPD, which revealed only a group difference.
This paper mainly concentrates on the robust multiple model adaptive controller performance assessment for switched systems via using the tensor *** multiple model adaptive control scheme is employed for the switched ...
详细信息
This paper mainly concentrates on the robust multiple model adaptive controller performance assessment for switched systems via using the tensor *** multiple model adaptive control scheme is employed for the switched control systems performance *** non-negative tensor factorization model is proposed to analyze the tensor data,which stems from uniqueness of low-rank decomposition of higher-order *** data-driven algorithms based on tensor space approach are derived for the calculation of performance measures by applying non-negativetensor *** is shown that the controller performance can be obtained by the data processing method of nonnegativetensor *** some sufficient conditions,such as a strong finite time switching,or a finite number of the dynamical subprocess,the closed-loop subprocess controller performance can be improved obviously for multivariate switched ***,a simulation example is presented to demonstrate the effectiveness of the proposed tensor approach by comparing the adaptive switching controller with other adaptive schemes or the single controller.
暂无评论