175 research outputs found
Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
In this work, we utilize T1-weighted MR images and StackNet to predict fluid
intelligence in adolescents. Our framework includes feature extraction, feature
normalization, feature denoising, feature selection, training a StackNet, and
predicting fluid intelligence. The extracted feature is the distribution of
different brain tissues in different brain parcellation regions. The proposed
StackNet consists of three layers and 11 models. Each layer uses the
predictions from all previous layers including the input layer. The proposed
StackNet is tested on a public benchmark Adolescent Brain Cognitive Development
Neurocognitive Prediction Challenge 2019 and achieves a mean squared error of
82.42 on the combined training and validation set with 10-fold
cross-validation. In addition, the proposed StackNet also achieves a mean
squared error of 94.25 on the testing data. The source code is available on
GitHub.Comment: 8 pages, 2 figures, 3 tables, Accepted by MICCAI ABCD-NP Challenge
2019; Added ND
Combining and Steganography of 3D Face Textures
One of the serious issues in communication between people is hiding
information from others, and the best way for this, is deceiving them. Since
nowadays face images are mostly used in three dimensional format, in this paper
we are going to steganography 3D face images, detecting which by curious people
will be impossible. As in detecting face only its texture is important, we
separate texture from shape matrices, for eliminating half of the extra
information, steganography is done only for face texture, and for
reconstructing 3D face, we can use any other shape. Moreover, we will indicate
that, by using two textures, how two 3D faces can be combined. For a complete
description of the process, first, 2D faces are used as an input for building
3D faces, and then 3D textures are hidden within other images.Comment: 6 pages, 10 figures, 16 equations, 5 section
Selecting the rank of truncated SVD by Maximum Approximation Capacity
Truncated Singular Value Decomposition (SVD) calculates the closest rank-
approximation of a given input matrix. Selecting the appropriate rank
defines a critical model order choice in most applications of SVD. To obtain a
principled cut-off criterion for the spectrum, we convert the underlying
optimization problem into a noisy channel coding problem. The optimal
approximation capacity of this channel controls the appropriate strength of
regularization to suppress noise. In simulation experiments, this information
theoretic method to determine the optimal rank competes with state-of-the art
model selection techniques.Comment: 7 pages, 5 figures; Will be presented at the IEEE International
Symposium on Information Theory (ISIT) 2011. The conference version has only
5 pages. This version has an extended appendi
Noise and nonlinearities in high-throughput data
High-throughput data analyses are becoming common in biology, communications,
economics and sociology. The vast amounts of data are usually represented in
the form of matrices and can be considered as knowledge networks. Spectra-based
approaches have proved useful in extracting hidden information within such
networks and for estimating missing data, but these methods are based
essentially on linear assumptions. The physical models of matching, when
applicable, often suggest non-linear mechanisms, that may sometimes be
identified as noise. The use of non-linear models in data analysis, however,
may require the introduction of many parameters, which lowers the statistical
weight of the model. According to the quality of data, a simpler linear
analysis may be more convenient than more complex approaches.
In this paper, we show how a simple non-parametric Bayesian model may be used
to explore the role of non-linearities and noise in synthetic and experimental
data sets.Comment: 12 pages, 3 figure
Capture of manufacturing uncertainty in turbine blades through probabilistic techniques
Efficient designing of the turbine blades is critical to the performance of an aircraft engine.
An area of significant research interest is the capture of manufacturing uncertainty in the
shapes of these turbine blades. The available data used for estimation of this manufacturing
uncertainty inevitably contains the effects of measurement error/noise. In the present work,
we propose the application of Principal Component Analysis (PCA) for de-noising the
measurement data and quantifying the underlying manufacturing uncertainty. Once the
PCA is performed, a method for dimensionality reduction has been proposed which utilizes
prior information available on the variance of measurement error for different
measurement types. Numerical studies indicate that approximately 82% of the variation in
the measurements from their design values is accounted for by the manufacturing
uncertainty, while the remaining 18% variation is filtered out as measurement error
Exact Dimensionality Selection for Bayesian PCA
We present a Bayesian model selection approach to estimate the intrinsic
dimensionality of a high-dimensional dataset. To this end, we introduce a novel
formulation of the probabilisitic principal component analysis model based on a
normal-gamma prior distribution. In this context, we exhibit a closed-form
expression of the marginal likelihood which allows to infer an optimal number
of components. We also propose a heuristic based on the expected shape of the
marginal likelihood curve in order to choose the hyperparameters. In
non-asymptotic frameworks, we show on simulated data that this exact
dimensionality selection approach is competitive with both Bayesian and
frequentist state-of-the-art methods
Model order selection criteria: comparative study and applications
A practical application of information theoretic criteria is presented in this paper. Eigenvalue decomposition of the signal correlation matrixbased AIC, MDL and MIBS criteria are investigated and used for online estimation of time varying parameters of harmonic signals in power systems.===PL===Artykuł przedstawia kryteria i porównanie metod redukcji modelu procesu. Przedstawiono i porównano różne kryteria bazujące na dekompozycji macierzy korelacji według wartości własnych: AIC, MDL i MIBS. Porównania dokonano na sygnałach harmonicznych odpowiadających układowi niestacjonarnemu
Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood
We discuss the problem of estimating the number of principal components in
Principal Com- ponents Analysis (PCA). Despite of the importance of the problem
and the multitude of solutions proposed in the literature, it comes as a
surprise that there does not exist a coherent asymptotic framework which would
justify different approaches depending on the actual size of the data set. In
this paper we address this issue by presenting an approximate Bayesian approach
based on Laplace approximation and introducing a general method for building
the model selection criteria, called PEnalized SEmi-integrated Likelihood
(PESEL). Our general framework encompasses a variety of existing approaches
based on probabilistic models, like e.g. Bayesian Information Criterion for the
Probabilistic PCA (PPCA), and allows for construction of new criteria,
depending on the size of the data set at hand. Specifically, we define PESEL
when the number of variables substantially exceeds the number of observations.
We also report results of extensive simulation studies and real data analysis,
which illustrate good properties of our proposed criteria as compared to the
state-of- the-art methods and very recent proposals. Specifially, these
simulations show that PESEL based criteria can be quite robust against
deviations from the probabilistic model assumptions. Selected PESEL based
criteria for the estimation of the number of principal components are
implemented in R package varclust, which is available on github
(https://github.com/psobczyk/varclust).Comment: 31 pages, 7 figure
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