25,854 research outputs found
Multidimensional Scaling on Multiple Input Distance Matrices
Multidimensional Scaling (MDS) is a classic technique that seeks vectorial
representations for data points, given the pairwise distances between them.
However, in recent years, data are usually collected from diverse sources or
have multiple heterogeneous representations. How to do multidimensional scaling
on multiple input distance matrices is still unsolved to our best knowledge. In
this paper, we first define this new task formally. Then, we propose a new
algorithm called Multi-View Multidimensional Scaling (MVMDS) by considering
each input distance matrix as one view. Our algorithm is able to learn the
weights of views (i.e., distance matrices) automatically by exploring the
consensus information and complementary nature of views. Experimental results
on synthetic as well as real datasets demonstrate the effectiveness of MVMDS.
We hope that our work encourages a wider consideration in many domains where
MDS is needed
Disaggregating Input-Output Tables by the Multidimensional RAS Method
An unknown input-output table can be estimated by the RAS method when only
its row and column sums are known and some initial structure is assumed. The
RAS approach can also be utilized for disaggregation of an annual national
table to more detailed tables such as regional, quarterly and domestic/imported
tables. However, the regular RAS method does not ensure that the sums of
disaggregated tables are equal to the total table. For this problem, we propose
to use the multidimensional RAS method which besides input and output totals
also ensures regional, quarterly and domestic/imported totals. Our analysis of
the Czech industry shows that the multidimensional RAS method increases the
accuracy of table estimation as well as accuracy of input-output applications
such as the Leontief inverse, the regional Isard's model and the quarterly
value added
Multidimensional Scaling Using Majorization: SMACOF in R
In this paper we present the methodology of multidimensional scaling problems (MDS) solved by means of the majorization algorithm. The objective function to be minimized is known as stress and functions which majorize stress are elaborated. This strategy to solve MDS problems is called SMACOF and it is implemented in an R package of the same name which is presented in this article. We extend the basic SMACOF theory in terms of configuration constraints, three-way data, unfolding models, and projection of the resulting configurations onto spheres and other quadratic surfaces. Various examples are presented to show the possibilities of the SMACOF approach offered by the corresponding package.
Precoding for Outage Probability Minimization on Block Fading Channels
The outage probability limit is a fundamental and achievable lower bound on
the word error rate of coded communication systems affected by fading. This
limit is mainly determined by two parameters: the diversity order and the
coding gain. With linear precoding, full diversity on a block fading channel
can be achieved without error-correcting code. However, the effect of precoding
on the coding gain is not well known, mainly due to the complicated expression
of the outage probability. Using a geometric approach, this paper establishes
simple upper bounds on the outage probability, the minimization of which yields
to precoding matrices that achieve very good performance. For discrete
alphabets, it is shown that the combination of constellation expansion and
precoding is sufficient to closely approach the minimum possible outage
achieved by an i.i.d. Gaussian input distribution, thus essentially maximizing
the coding gain.Comment: Submitted to Transactions on Information Theory on March 23, 201
Empirical Analysis of the Necessary and Sufficient Conditions of the Echo State Property
The Echo State Network (ESN) is a specific recurrent network, which has
gained popularity during the last years. The model has a recurrent network
named reservoir, that is fixed during the learning process. The reservoir is
used for transforming the input space in a larger space. A fundamental property
that provokes an impact on the model accuracy is the Echo State Property (ESP).
There are two main theoretical results related to the ESP. First, a sufficient
condition for the ESP existence that involves the singular values of the
reservoir matrix. Second, a necessary condition for the ESP. The ESP can be
violated according to the spectral radius value of the reservoir matrix. There
is a theoretical gap between these necessary and sufficient conditions. This
article presents an empirical analysis of the accuracy and the projections of
reservoirs that satisfy this theoretical gap. It gives some insights about the
generation of the reservoir matrix. From previous works, it is already known
that the optimal accuracy is obtained near to the border of stability control
of the dynamics. Then, according to our empirical results, we can see that this
border seems to be closer to the sufficient conditions than to the necessary
conditions of the ESP.Comment: 23 pages, 14 figures, accepted paper for the IEEE IJCNN, 201
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