12 research outputs found
Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series
In this paper we investigate the influence of external
noise on the inference of network structures. The purpose of our
simulations is to gain insights in the experimental design of microarray
experiments to infer, e.g., transcription regulatory networks
from microarray experiments. Here external noise means, that the
dynamics of the system under investigation, e.g., temporal changes of
mRNA concentration, is affected by measurement errors. Additionally
to external noise another problem occurs in the context of microarray
experiments. Practically, it is not possible to monitor the mRNA
concentration over an arbitrary long time period as demanded by the
statistical methods used to learn the underlying network structure. For
this reason, we use only short time series to make our simulations
more biologically plausible
On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics
In this paper we present a learning-based approach for the modelling of complex movement sequences. Based on the method of Spatio-Temporal Morphable Models (STMMS. We derive a hierarchical algorithm that, in a first step, identifies automatically movement elements in movement sequences based on a coarse spatio-temporal description, and in a second step models these movement primitives by approximation through linear combinations of learned example movement trajectories. We describe the different steps of the algorithm and show how it can be applied for modelling and synthesis of complex sequences of human movements that contain movement elements with variable style. The proposed method is demonstrated on different applications of movement representation relevant for imitation learning of movement styles in humanoid robotics
Missing data estimation using polynomial kernels
In this paper, we deal with the problem of partially observed objects. These objects are defined by a set of points and their shape variations are represented by a statistical model. We presents two model in this paper : a linear model based on PCA and a non-linear model based on KPCA. The present work attempts to localize of non visible parts of an object, from the visible part and from the model, using the variability represented by the models. Both are applied to synthesis data and to cephalometric data with good results
Learning to Find Pre-Images
We consider the problem of reconstructing patterns from a feature
map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express
their solutions in terms of input points mapped into the RKHS. We
introduce a technique based on kernel principal component analysis
and regression to reconstruct corresponding patterns in the input space (aka pre-images) and review
its performance in several applications requiring
the construction of pre-images. The introduced technique avoids
difficult and/or unstable numerical optimization, is easy to
implement and, unlike previous methods, permits the computation of pre-images
in discrete input spaces
Learning to Find Graph Pre-Images
The recent development of graph kernel functions
has made it possible to apply well-established
machine learning methods to graphs.
However, to allow for analyses that yield a graph as a result, it is necessary to solve the so-called pre-image problem: to reconstruct a graph from its feature space representation induced by the kernel. Here, we suggest a practical solution to this problem
U‐shaped frequency selective surfaces for single‐ and dual‐band applications together with absorber and sensor configurations
In this study, the frequency selective surface (FSS), absorbance and sensing applications of new unit cells comprised of single, double and quad U-shaped metallic elements are numerically and experimentally investigated. The numerical results are realised by using finite integration technique-based electromagnetic simulation software, CST Microwave Studio. Experimental results are obtained by means of Agilent N5234A PNA-L vector network analyser. The simulated and measured results of U-shaped FSSs are carried out by using two different ways; periodic unit cell and waveguide boundary conditions. The single and quad U-shaped FSS elements show a single band-stop filter property, whereas the double U-shaped FSS shows a dual-frequency band-stop filter behaviour. Absorber and sensor applications of the proposed U-shaped FSSs are illustrated both numerically and experimentally. The three types of U-shaped FSS structures show a narrowband absorbance which has potential in biomedical and sensing applications. Single and dual absorber characteristics are observed with a 90% absorbance. For the sensor configuration, 800 MHz bandwidth is obtained for sensing different materials showing that it is possible to realise different microwave sensor studies in industries of food and agriculture. Simulated and measured results are compared for FSS, absorber and sensor configurations, and the results show a good agreement with each other
Efficient Approximations for Support Vector Machines in Object Detection
We present a new approximation scheme for support vector decision
functions in object detection. In the present approach we are
building on an existing algorithm where the set of support vectors
is replaced by a smaller so-called reduced set of synthetic
points. Instead of finding the reduced set via unconstrained
optimization, we impose a structural constraint on the synthetic
vectors such that the resulting approximation can be evaluated via
separable filters. Applications that require scanning an entire
image can benefit from this representation: when using separable
filters, the average computational complexity for evaluating a
reduced set vector on a test patch of size (h x w) drops from
O(hw) to O(h+w). We show experimental results on
handwritten digits and face detection
Multivariate Regression via Stiefel Manifold Constraints
We introduce a learning technique for regression
between high-dimensional spaces. Standard methods typically reduce
this task to many one-dimensional problems, with each output
dimension considered independently. By contrast, in our approach
the feature construction and the regression estimation are
performed jointly, directly minimizing a loss function that we
specify, subject to a rank constraint. A major advantage of this
approach is that the loss is no longer chosen according to the
algorithmic requirements, but can be tailored to the
characteristics of the task at hand; the features will then be
optimal with respect to this objective, and dependence between the
outputs can be exploited