15,268 research outputs found
A literature survey of matrix methods for data science
Efficient numerical linear algebra is a core ingredient in many applications
across almost all scientific and industrial disciplines. With this survey we
want to illustrate that numerical linear algebra has played and is playing a
crucial role in enabling and improving data science computations with many new
developments being fueled by the availability of data and computing resources.
We highlight the role of various different factorizations and the power of
changing the representation of the data as well as discussing topics such as
randomized algorithms, functions of matrices, and high-dimensional problems. We
briefly touch upon the role of techniques from numerical linear algebra used
within deep learning
On algorithmization of Janashia-Lagvilava matrix spectral factorization method
We consider three different ways of algorithmization of the
Janashia-Lagvilava spectral factorization method. The first algorithm is faster
than the second one, however, it is only suitable for matrices of low
dimension. The second algorithm, on the other hand, can be applied to matrices
of substantially larger dimension. The third algorithm is a superfast
implementation of the method, but only works in the polynomial case under the
additional restriction that the zeros of the determinant are not too close to
the boundary. All three algorithms fully utilize the advantage of the method
which carries out spectral factorization of leading principal submatrices
step-by-step. The corresponding results of numerical simulations are reported
in order to describe the characteristic features of each algorithm and compare
them to other existing algorithms.Comment: 18 page
A Survey on Multi-View Clustering
With advances in information acquisition technologies, multi-view data become
ubiquitous. Multi-view learning has thus become more and more popular in
machine learning and data mining fields. Multi-view unsupervised or
semi-supervised learning, such as co-training, co-regularization has gained
considerable attention. Although recently, multi-view clustering (MVC) methods
have been developed rapidly, there has not been a survey to summarize and
analyze the current progress. Therefore, this paper reviews the common
strategies for combining multiple views of data and based on this summary we
propose a novel taxonomy of the MVC approaches. We further discuss the
relationships between MVC and multi-view representation, ensemble clustering,
multi-task clustering, multi-view supervised and semi-supervised learning.
Several representative real-world applications are elaborated. To promote
future development of MVC, we envision several open problems that may require
further investigation and thorough examination.Comment: 17 pages, 4 figure
On the estimation of the convergence rate in the Janashia-Lagvilava spectral factorization algorithm
In the present paper, we estimate the convergence rate in the
Janashia-Lagvilava spectral factorization algorithm (see Studia Mathematica,
137, 1999, 93-100) under the restriction on a spectral density matrix that its
inverse is integrable.Comment: 9 page
A New Method of Matrix Spectral Factorization
A new method of matrix spectral factorization is proposed which reliably
computes an approximate spectral factor of any matrix spectral density that
admits spectral factorizationComment: 23 page
Unmixing of Hyperspectral Data Using Robust Statistics-based NMF
Mixed pixels are presented in hyperspectral images due to low spatial
resolution of hyperspectral sensors. Spectral unmixing decomposes mixed pixels
spectra into endmembers spectra and abundance fractions. In this paper using of
robust statistics-based nonnegative matrix factorization (RNMF) for spectral
unmixing of hyperspectral data is investigated. RNMF uses a robust cost
function and iterative updating procedure, so is not sensitive to outliers.
This method has been applied to simulated data using USGS spectral library,
AVIRIS and ROSIS datasets. Unmixing results are compared to traditional NMF
method based on SAD and AAD measures. Results demonstrate that this method can
be used efficiently for hyperspectral unmixing purposes.Comment: 4 pages, conferenc
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
There has been a surge of recent interest in learning representations for
graph-structured data. Graph representation learning methods have generally
fallen into three main categories, based on the availability of labeled data.
The first, network embedding (such as shallow graph embedding or graph
auto-encoders), focuses on learning unsupervised representations of relational
structure. The second, graph regularized neural networks, leverages graphs to
augment neural network losses with a regularization objective for
semi-supervised learning. The third, graph neural networks, aims to learn
differentiable functions over discrete topologies with arbitrary structure.
However, despite the popularity of these areas there has been surprisingly
little work on unifying the three paradigms. Here, we aim to bridge the gap
between graph neural networks, network embedding and graph regularization
models. We propose a comprehensive taxonomy of representation learning methods
for graph-structured data, aiming to unify several disparate bodies of work.
Specifically, we propose a Graph Encoder Decoder Model (GRAPHEDM), which
generalizes popular algorithms for semi-supervised learning on graphs (e.g.
GraphSage, Graph Convolutional Networks, Graph Attention Networks), and
unsupervised learning of graph representations (e.g. DeepWalk, node2vec, etc)
into a single consistent approach. To illustrate the generality of this
approach, we fit over thirty existing methods into this framework. We believe
that this unifying view both provides a solid foundation for understanding the
intuition behind these methods, and enables future research in the area
The Why and How of Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) has become a widely used tool for the
analysis of high-dimensional data as it automatically extracts sparse and
meaningful features from a set of nonnegative data vectors. We first illustrate
this property of NMF on three applications, in image processing, text mining
and hyperspectral imaging --this is the why. Then we address the problem of
solving NMF, which is NP-hard in general. We review some standard NMF
algorithms, and also present a recent subclass of NMF problems, referred to as
near-separable NMF, that can be solved efficiently (that is, in polynomial
time), even in the presence of noise --this is the how. Finally, we briefly
describe some problems in mathematics and computer science closely related to
NMF via the nonnegative rank.Comment: 25 pages, 5 figures. Some typos and errors corrected, Section 3.2
reorganize
Introduction to Nonnegative Matrix Factorization
In this paper, we introduce and provide a short overview of nonnegative
matrix factorization (NMF). Several aspects of NMF are discussed, namely, the
application in hyperspectral imaging, geometry and uniqueness of NMF solutions,
complexity, algorithms, and its link with extended formulations of polyhedra.
In order to put NMF into perspective, the more general problem class of
constrained low-rank matrix approximation problems is first briefly introduced.Comment: 18 pages, 4 figure
An Analytic Proof of the Matrix Spectral Factorization Theorem
An analytic proof is proposed of Wiener's theorem on factorization of
positive definite matrix-functions.Comment: 11 page
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