24,109 research outputs found
Oracle Inequalities and Optimal Inference under Group Sparsity
We consider the problem of estimating a sparse linear regression vector
under a gaussian noise model, for the purpose of both prediction and
model selection. We assume that prior knowledge is available on the sparsity
pattern, namely the set of variables is partitioned into prescribed groups,
only few of which are relevant in the estimation process. This group sparsity
assumption suggests us to consider the Group Lasso method as a means to
estimate . We establish oracle inequalities for the prediction and
estimation errors of this estimator. These bounds hold under a
restricted eigenvalue condition on the design matrix. Under a stronger
coherence condition, we derive bounds for the estimation error for mixed
-norms with . When , this result implies
that a threshold version of the Group Lasso estimator selects the sparsity
pattern of with high probability. Next, we prove that the rate of
convergence of our upper bounds is optimal in a minimax sense, up to a
logarithmic factor, for all estimators over a class of group sparse vectors.
Furthermore, we establish lower bounds for the prediction and
estimation errors of the usual Lasso estimator. Using this result, we
demonstrate that the Group Lasso can achieve an improvement in the prediction
and estimation properties as compared to the Lasso.Comment: 37 page
Estimation of a sparse group of sparse vectors
We consider a problem of estimating a sparse group of sparse normal mean
vectors. The proposed approach is based on penalized likelihood estimation with
complexity penalties on the number of nonzero mean vectors and the numbers of
their "significant" components, and can be performed by a computationally fast
algorithm. The resulting estimators are developed within Bayesian framework and
can be viewed as MAP estimators. We establish their adaptive minimaxity over a
wide range of sparse and dense settings. The presented short simulation study
demonstrates the efficiency of the proposed approach that successfully competes
with the recently developed sparse group lasso estimator
Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device
There is a significant high fall risk population, where individuals are susceptible to frequent falls and obtaining significant injury, where quick medical response and fall information are critical to providing efficient aid. This article presents an evaluation of compressive sensing techniques in an accelerometer-based intelligent fall detection system modelled on a wearable Shimmer biomedical embedded computing device with Matlab. The presented fall detection system utilises a database of fall and activities of daily living signals evaluated with discrete wavelet transforms and principal component analysis to obtain binary tree classifiers for fall evaluation. 14 test subjects undertook various fall and activities of daily living experiments with a Shimmer device to generate data for principal component analysis-based fall classifiers and evaluate the proposed fall analysis system. The presented system obtains highly accurate fall detection results, demonstrating significant advantages in comparison with the thresholding method presented. Additionally, the presented approach offers advantageous fall diagnostic information. Furthermore, transmitted data accounts for over 80% battery current usage of the Shimmer device, hence it is critical the acceleration data is reduced to increase transmission efficiency and in-turn improve battery usage performance. Various Matching pursuit-based compressive sensing techniques have been utilised to significantly reduce acceleration information required for transmission.Scopu
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