… feature, principal component … features present in the data can be learned using DMF.
Leveraging the recent advancements in DMF, we propose a novel deep sparse representation (…

… Because of variability and complexity of fault feature and numerous … component analysis
method based on sparse representation theory for these reasons (1) sparse representation has …

… We use sparse coefficients as image features. The source … innovation sparse coefficients by
joint sparse representation. The … as combination of two components: the common component …

… of sparse representation in brain signal processing, including components extraction, BSS
… [9] to conduct sparse representation and obtain the OMP-based feature selection algorithm. …

… component analysis (ICA) with sparse representation-based … We use ICA to extract useful
features from heartbeats. A … to compute a sparse representation of a test feature vector with …

… On the one hand, if the commonality component is left aside in our tracking model (6),
learning the featurespecific components of each feature is equivalent to learning multiple 1 …

… component are statistically independent which allows us to analysis each feature channel
… Besides, from visual observation, we find that some of the independent components appear …

… the true value of the large amplitude components. The ℓ 2 norm minimization cannot
acquire the sparse solution, but it can simplify the sparse representation model. To address the …

… features of the training samples and test samples. Then, we use the adaptive weighted joint
sparse representation … problem of deep features, we use the principal component analysis (…

… of two major components— feature extraction and sparse representation … feature, i-vector,
and SRC. To combine i-vector and SRC sufficiently, we use probabilistic principal component …