System Identification I

Ensemble methods are considered the state‐of‐the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state‐of‐the‐art ensemble methods and discusses current challenges and trends in the field.

Ensemble learning: A survey

Purpose Parallel imaging allows the reconstruction of images from undersampled multicoil data. The two main approaches are: SENSE, which explicitly uses coil sensitivities, and GRAPPA, which makes use of learned correlations in k-space. The purpose of this work is to clarify their relationship and to develop and evaluate an improved algorithm. Theory and methods A theoretical analysis shows: (1) The correlations in k-space are encoded in the null space of a calibration matrix. (2) Both approaches restrict the solution to a subspace spanned by the sensitivities. (3) The sensitivities appear as the main eigenvector of a reconstruction operator computed from the null space. The basic assumptions and the quality of the sensitivity maps are evaluated in experimental examples. The appearance of additional eigenvectors motivates an extended SENSE reconstruction with multiple maps, which is compared to existing methods. Results The existence of a null space and the high quality of the extracted sensitivities are confirmed. The extended reconstruction combines all advantages of SENSE with robustness to certain errors similar to GRAPPA. Conclusion In this article the gap between both approaches is finally bridged. A new autocalibration technique combines the benefits of both.

/pdf/espirit-an-eigenvalue-approach-to-autocalibrating-parallel-3so4l38igs.pdf

ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA.

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Magnetic Resonance Imaging Physical Principles And Sequence Design

A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction. Extensive empirical studies are needed to search for an optimal combination of feature representation, feature set, and classifier to achieve good recognition performance. This paper presents a novel Boosted Deep Belief Network (BDBN) for performing the three training stages iteratively in a unified loopy framework. Through the proposed BDBN framework, a set of features, which is effective to characterize expression-related facial appearance/shape changes, can be learned and selected to form a boosted strong classifier in a statistical way. As learning continues, the strong classifier is improved iteratively and more importantly, the discriminative capabilities of selected features are strengthened as well according to their relative importance to the strong classifier via a joint fine-tune process in the BDBN framework. Extensive experiments on two public databases showed that the BDBN framework yielded dramatic improvements in facial expression analysis.

Facial Expression Recognition via a Boosted Deep Belief Network

Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm

In sensitivity encoding reconstruction, the issue of ill conditioning
becomes serious and thus the signal-to-noise ratio
becomes poor when a large acceleration factor is employed.
Total variation (TV) regularization has been used to address
this issue and shown to better preserve sharp edges than
Tikhonov regularization but may cause blocky effect. In this article,
we study nonlocal TV regularization for noise suppression
in sensitivity encoding reconstruction. The nonlocal TV regularization
method extends the conventional TV norm to a nonlocal
version by introducing a weighted nonlocal gradient
function calculated from the weighted difference between the
target pixel and its generalized neighbors, where the weights
incorporate the prior information of the image structure. The
method not only inherits the edge-preserving advantage of TV
regularization but also overcomes the blocky effect. The experimental
results from in vivo data show that nonlocal TV regularization
is superior to the existing competing methods in
preserving fine details and reducing noise and artifacts.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.22736

Sensitivity encoding reconstruction with nonlocal total variation regularization.

GRAPPA linearly combines the undersampled k-space signals
to estimate the missing k-space signals where the coefficients
are obtained by fitting to some auto-calibration signals
(ACS) sampled with Nyquist rate based on the shift-invariant
property. At high acceleration factors, GRAPPA reconstruction
can suffer from a high level of noise even with a large number
of auto-calibration signals. In this work, we propose a nonlinear
method to improve GRAPPA. The method is based on the
so-called kernel method which is widely used in machine
learning. Specifically, the undersampled k-space signals are
mapped through a nonlinear transform to a high-dimensional
feature space, and then linearly combined to reconstruct the
missing k-space data. The linear combination coefficients are
also obtained through fitting to the ACS data but in the new
feature space. The procedure is equivalent to adding many
virtual channels in reconstruction. A polynomial kernel with
explicit mapping functions is investigated in this work. Experimental
results using phantom and in vivo data demonstrate
that the proposed nonlinear GRAPPA method can significantly
improve the reconstruction quality over GRAPPA and its
state-of-the-art derivatives.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.23279

Yuchou Chang

Papers

Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm

Sensitivity encoding reconstruction with nonlocal total variation regularization.

Nonlinear GRAPPA: A kernel approach to parallel MRI reconstruction

A facial expression recognition system based on supervised locally linear embedding

Consensus unsupervised feature ranking from multiple views