561 research outputs found
Localized Lasso for High-Dimensional Regression
We introduce the localized Lasso, which is suited for learning models that
are both interpretable and have a high predictive power in problems with high
dimensionality and small sample size . More specifically, we consider a
function defined by local sparse models, one at each data point. We introduce
sample-wise network regularization to borrow strength across the models, and
sample-wise exclusive group sparsity (a.k.a., norm) to introduce
diversity into the choice of feature sets in the local models. The local models
are interpretable in terms of similarity of their sparsity patterns. The cost
function is convex, and thus has a globally optimal solution. Moreover, we
propose a simple yet efficient iterative least-squares based optimization
procedure for the localized Lasso, which does not need a tuning parameter, and
is guaranteed to converge to a globally optimal solution. The solution is
empirically shown to outperform alternatives for both simulated and genomic
personalized medicine data
Multi-pooling 3D Convolutional Neural Network for fMRI Classification of Visual Brain States
Neural decoding of visual object classification via functional magnetic
resonance imaging (fMRI) data is challenging and is vital to understand
underlying brain mechanisms. This paper proposed a multi-pooling 3D
convolutional neural network (MP3DCNN) to improve fMRI classification accuracy.
MP3DCNN is mainly composed of a three-layer 3DCNN, where the first and second
layers of 3D convolutions each have a branch of pooling connection. The results
showed that this model can improve the classification accuracy for categorical
(face vs. object), face sub-categorical (male face vs. female face), and object
sub-categorical (natural object vs. artificial object) classifications from
1.684% to 14.918% over the previous study in decoding brain mechanisms
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