171,806 research outputs found
Unsupervised spectral decomposition of X-ray binaries with application to GX 339-4
In this paper we explore unsupervised spectral decomposition methods for
distinguishing the effect of different spectral components for a set of
consecutive spectra from an X-ray binary. We use well-established linear
methods for the decomposition, namely principal component analysis, independent
component analysis and non-negative matrix factorisation (NMF). Applying these
methods to a simulated dataset consisting of a variable multicolour disc black
body and a cutoff power law, we find that NMF outperforms the other two methods
in distinguishing the spectral components. In addition, due the non-negative
nature of NMF, the resulting components may be fitted separately, revealing the
evolution of individual parameters. To test the NMF method on a real source, we
analyse data from the low-mass X-ray binary GX 339-4 and found the results to
match those of previous studies. In addition, we found the inner radius of the
accretion disc to be located at the innermost stable circular orbit in the
intermediate state right after the outburst peak. This study shows that using
unsupervised spectral decomposition methods results in detecting the separate
component fluxes down to low flux levels. Also, these methods provide an
alternative way of detecting the spectral components without performing actual
spectral fitting, which may prove to be practical when dealing with large
datasets.Comment: 12 pages, 13 figure
A challenge for critical point of spin glass in ground state
We show several calculations to identify the critical point in the ground
state in random spin systems including spin glasses on the basis of the duality
analysis. The duality analysis is a profound method to obtain the precise
location of the critical point in finite temperature even for spin glasses. We
propose a single equality for identifying the critical point in the ground
state from several speculations. The equality can indeed give the exact
location of the critical points for the bond-dilution Ising model on several
lattices and provides insight on further analysis on the ground state in spin
glasses.Comment: 7 pages, 2 figures, to appear in Proceedings of 4th YSM-SPIP (Sendai,
14-16 December 2012
Decomposable Principal Component Analysis
We consider principal component analysis (PCA) in decomposable Gaussian
graphical models. We exploit the prior information in these models in order to
distribute its computation. For this purpose, we reformulate the problem in the
sparse inverse covariance (concentration) domain and solve the global
eigenvalue problem using a sequence of local eigenvalue problems in each of the
cliques of the decomposable graph. We demonstrate the application of our
methodology in the context of decentralized anomaly detection in the Abilene
backbone network. Based on the topology of the network, we propose an
approximate statistical graphical model and distribute the computation of PCA
Representing complex data using localized principal components with application to astronomical data
Often the relation between the variables constituting a multivariate data
space might be characterized by one or more of the terms: ``nonlinear'',
``branched'', ``disconnected'', ``bended'', ``curved'', ``heterogeneous'', or,
more general, ``complex''. In these cases, simple principal component analysis
(PCA) as a tool for dimension reduction can fail badly. Of the many alternative
approaches proposed so far, local approximations of PCA are among the most
promising. This paper will give a short review of localized versions of PCA,
focusing on local principal curves and local partitioning algorithms.
Furthermore we discuss projections other than the local principal components.
When performing local dimension reduction for regression or classification
problems it is important to focus not only on the manifold structure of the
covariates, but also on the response variable(s). Local principal components
only achieve the former, whereas localized regression approaches concentrate on
the latter. Local projection directions derived from the partial least squares
(PLS) algorithm offer an interesting trade-off between these two objectives. We
apply these methods to several real data sets. In particular, we consider
simulated astrophysical data from the future Galactic survey mission Gaia.Comment: 25 pages. In "Principal Manifolds for Data Visualization and
Dimension Reduction", A. Gorban, B. Kegl, D. Wunsch, and A. Zinovyev (eds),
Lecture Notes in Computational Science and Engineering, Springer, 2007, pp.
180--204,
http://www.springer.com/dal/home/generic/search/results?SGWID=1-40109-22-173750210-
A Detailed Investigation into Low-Level Feature Detection in Spectrogram Images
Being the first stage of analysis within an image, low-level feature detection is a crucial step in the image analysis process and, as such, deserves suitable attention. This paper presents a systematic investigation into low-level feature detection in spectrogram images. The result of which is the identification of frequency tracks. Analysis of the literature identifies different strategies for accomplishing low-level feature detection. Nevertheless, the advantages and disadvantages of each are not explicitly investigated. Three model-based detection strategies are outlined, each extracting an increasing amount of information from the spectrogram, and, through ROC analysis, it is shown that at increasing levels of extraction the detection rates increase. Nevertheless, further investigation suggests that model-based detection has a limitation—it is not computationally feasible to fully evaluate the model of even a simple sinusoidal track. Therefore, alternative approaches, such as dimensionality reduction, are investigated to reduce the complex search space. It is shown that, if carefully selected, these techniques can approach the detection rates of model-based strategies that perform the same level of information extraction. The implementations used to derive the results presented within this paper are available online from http://stdetect.googlecode.com
Mesh-based Autoencoders for Localized Deformation Component Analysis
Spatially localized deformation components are very useful for shape analysis
and synthesis in 3D geometry processing. Several methods have recently been
developed, with an aim to extract intuitive and interpretable deformation
components. However, these techniques suffer from fundamental limitations
especially for meshes with noise or large-scale deformations, and may not
always be able to identify important deformation components. In this paper we
propose a novel mesh-based autoencoder architecture that is able to cope with
meshes with irregular topology. We introduce sparse regularization in this
framework, which along with convolutional operations, helps localize
deformations. Our framework is capable of extracting localized deformation
components from mesh data sets with large-scale deformations and is robust to
noise. It also provides a nonlinear approach to reconstruction of meshes using
the extracted basis, which is more effective than the current linear
combination approach. Extensive experiments show that our method outperforms
state-of-the-art methods in both qualitative and quantitative evaluations
Non-negative mixtures
This is the author's accepted pre-print of the article, first published as M. D. Plumbley, A. Cichocki and R. Bro. Non-negative mixtures. In P. Comon and C. Jutten (Ed), Handbook of Blind Source Separation: Independent Component Analysis and Applications. Chapter 13, pp. 515-547. Academic Press, Feb 2010. ISBN 978-0-12-374726-6 DOI: 10.1016/B978-0-12-374726-6.00018-7file: Proof:p\PlumbleyCichockiBro10-non-negative.pdf:PDF owner: markp timestamp: 2011.04.26file: Proof:p\PlumbleyCichockiBro10-non-negative.pdf:PDF owner: markp timestamp: 2011.04.2
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