3 research outputs found
Randomized Structural Sparsity via Constrained Block Subsampling for Improved Sensitivity of Discriminative Voxel Identification
In this paper, we consider voxel selection for functional Magnetic Resonance
Imaging (fMRI) brain data with the aim of finding a more complete set of
probably correlated discriminative voxels, thus improving interpretation of the
discovered potential biomarkers. The main difficulty in doing this is an
extremely high dimensional voxel space and few training samples, resulting in
unreliable feature selection. In order to deal with the difficulty, stability
selection has received a great deal of attention lately, especially due to its
finite sample control of false discoveries and transparent principle for
choosing a proper amount of regularization. However, it fails to make explicit
use of the correlation property or structural information of these
discriminative features and leads to large false negative rates. In other
words, many relevant but probably correlated discriminative voxels are missed.
Thus, we propose a new variant on stability selection "randomized structural
sparsity", which incorporates the idea of structural sparsity. Numerical
experiments demonstrate that our method can be superior in controlling for
false negatives while also keeping the control of false positives inherited
from stability selection
Multi-stage Multi-task feature learning via adaptive threshold
Multi-task feature learning aims to identity the shared features among tasks
to improve generalization. It has been shown that by minimizing non-convex
learning models, a better solution than the convex alternatives can be
obtained. Therefore, a non-convex model based on the capped-
regularization was proposed in \cite{Gong2013}, and a corresponding efficient
multi-stage multi-task feature learning algorithm (MSMTFL) was presented.
However, this algorithm harnesses a prescribed fixed threshold in the
definition of the capped- regularization and the lack of
adaptivity might result in suboptimal performance. In this paper we propose to
employ an adaptive threshold in the capped- regularized
formulation, where the corresponding variant of MSMTFL will incorporate an
additional component to adaptively determine the threshold value. This variant
is expected to achieve a better feature selection performance over the original
MSMTFL algorithm. In particular, the embedded adaptive threshold component
comes from our previously proposed iterative support detection (ISD) method
\cite{Wang2010}. Empirical studies on both synthetic and real-world data sets
demonstrate the effectiveness of this new variant over the original MSMTFL.Comment: 13 pages,12 figures. arXiv admin note: text overlap with
arXiv:1210.5806 by other author
Randomized Structural Sparsity based Support Identification with Applications to Locating Activated or Discriminative Brain Areas: A Multi-center Reproducibility Study
In this paper, we focus on how to locate the relevant or discriminative brain
regions related with external stimulus or certain mental decease, which is also
called support identification, based on the neuroimaging data. The main
difficulty lies in the extremely high dimensional voxel space and relatively
few training samples, easily resulting in an unstable brain region discovery
(or called feature selection in context of pattern recognition). When the
training samples are from different centers and have betweencenter variations,
it will be even harder to obtain a reliable and consistent result.
Corresponding, we revisit our recently proposed algorithm based on stability
selection and structural sparsity. It is applied to the multi-center MRI data
analysis for the first time. A consistent and stable result is achieved across
different centers despite the between-center data variation while many other
state-of-the-art methods such as two sample t-test fail. Moreover, we have
empirically showed that the performance of this algorithm is robust and
insensitive to several of its key parameters. In addition, the support
identification results on both functional MRI and structural MRI are
interpretable and can be the potential biomarkers.Comment: arXiv admin note: text overlap with arXiv:1410.465