932 research outputs found
Large-scale Dynamic Network Representation via Tensor Ring Decomposition
Large-scale Dynamic Networks (LDNs) are becoming increasingly important in
the Internet age, yet the dynamic nature of these networks captures the
evolution of the network structure and how edge weights change over time,
posing unique challenges for data analysis and modeling. A Latent Factorization
of Tensors (LFT) model facilitates efficient representation learning for a LDN.
But the existing LFT models are almost based on Canonical Polyadic
Factorization (CPF). Therefore, this work proposes a model based on Tensor Ring
(TR) decomposition for efficient representation learning for a LDN.
Specifically, we incorporate the principle of single latent factor-dependent,
non-negative, and multiplicative update (SLF-NMU) into the TR decomposition
model, and analyze the particular bias form of TR decomposition. Experimental
studies on two real LDNs demonstrate that the propose method achieves higher
accuracy than existing models
Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models
Nonnegative matrix factorization (NMF) is a powerful class of feature
extraction techniques that has been successfully applied in many fields, namely
in signal and image processing. Current NMF techniques have been limited to a
single-objective problem in either its linear or nonlinear kernel-based
formulation. In this paper, we propose to revisit the NMF as a multi-objective
problem, in particular a bi-objective one, where the objective functions
defined in both input and feature spaces are taken into account. By taking the
advantage of the sum-weighted method from the literature of multi-objective
optimization, the proposed bi-objective NMF determines a set of nondominated,
Pareto optimal, solutions instead of a single optimal decomposition. Moreover,
the corresponding Pareto front is studied and approximated. Experimental
results on unmixing real hyperspectral images confirm the efficiency of the
proposed bi-objective NMF compared with the state-of-the-art methods
Sparse general non-negative matrix factorization based on left semi-tensor product
The dimension reduction of large scale high-dimensional data is a challenging task, especially the dimension reduction of face data and the accuracy increment of face recognition in the large scale face recognition system, which may cause large storage space and long recognition time. In order to further reduce the recognition time and the storage space in the large scale face recognition systems, on the basis of the general non-negative matrix factorization based on left semi-tensor (GNMFL) without dimension matching constraints proposed in our previous work, we propose a sparse GNMFL/L (SGNMFL/L) to decompose a large number of face data sets in the large scale face recognition systems, which makes the decomposed base matrix sparser and suppresses the decomposed coefficient matrix. Therefore, the dimension of the basis matrix and the coefficient matrix can be further reduced. Two sets of experiments are conducted to show the effectiveness of the proposed SGNMFL/L on two databases. The experiments are mainly designed to verify the effects of two hyper-parameters on the sparseness of basis matrix factorized by SGNMFL/L, compare the performance of the conventional NMF, sparse NMF (SNMF), GNMFL, and the proposed SGNMFL/L in terms of storage space and time efficiency, and compare their face recognition accuracies with different noises. Both the theoretical derivation and the experimental results show that the proposed SGNMF/L can effectively save the storage space and reduce the computation time while achieving high recognition accuracy and has strong robustness
An Online Sparse Streaming Feature Selection Algorithm
Online streaming feature selection (OSFS), which conducts feature selection
in an online manner, plays an important role in dealing with high-dimensional
data. In many real applications such as intelligent healthcare platform,
streaming feature always has some missing data, which raises a crucial
challenge in conducting OSFS, i.e., how to establish the uncertain relationship
between sparse streaming features and labels. Unfortunately, existing OSFS
algorithms never consider such uncertain relationship. To fill this gap, we in
this paper propose an online sparse streaming feature selection with
uncertainty (OS2FSU) algorithm. OS2FSU consists of two main parts: 1) latent
factor analysis is utilized to pre-estimate the missing data in sparse
streaming features before con-ducting feature selection, and 2) fuzzy logic and
neighborhood rough set are employed to alleviate the uncertainty between
estimated streaming features and labels during conducting feature selection. In
the experiments, OS2FSU is compared with five state-of-the-art OSFS algorithms
on six real datasets. The results demonstrate that OS2FSU outperforms its
competitors when missing data are encountered in OSFS
Enhancing Compressed Sensing 4D Photoacoustic Tomography by Simultaneous Motion Estimation
A crucial limitation of current high-resolution 3D photoacoustic tomography
(PAT) devices that employ sequential scanning is their long acquisition time.
In previous work, we demonstrated how to use compressed sensing techniques to
improve upon this: images with good spatial resolution and contrast can be
obtained from suitably sub-sampled PAT data acquired by novel acoustic scanning
systems if sparsity-constrained image reconstruction techniques such as total
variation regularization are used. Now, we show how a further increase of image
quality can be achieved for imaging dynamic processes in living tissue (4D
PAT). The key idea is to exploit the additional temporal redundancy of the data
by coupling the previously used spatial image reconstruction models with
sparsity-constrained motion estimation models. While simulated data from a
two-dimensional numerical phantom will be used to illustrate the main
properties of this recently developed
joint-image-reconstruction-and-motion-estimation framework, measured data from
a dynamic experimental phantom will also be used to demonstrate their potential
for challenging, large-scale, real-world, three-dimensional scenarios. The
latter only becomes feasible if a carefully designed combination of tailored
optimization schemes is employed, which we describe and examine in more detail
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
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