Sparse autoencoder learns an overcomplete sparse representation of data in the hidden layer when the number of hidden units K are greater than the number of input units n, i.e., K>n.
However, this approach will give us a sparse set of data as it only triangulates keypoints that are distinctive.
Simulation results confirm that WAVinPOD preserves the dimensionality reduction obtained using POD, while improving its filtering properties through the sparse representation of data in wavelet basis.
The resulting basis vectors are generally localized to neighborhoods of varying size and may also lead to sparse representations of data on a graph [165].
The sparse locality of data belonging to different applications can be optimized by employing self-optimizing block reorganizing solutions like Borg [8].
This suggests that the data generation process may be misled by the sparse distribution of data points.
In fact, the sparse amount of data available suggests that the activity of the CBB-cycle declines rather rapidly.
However, in others [ 12, 15], there was sparse interpretation of data and little consideration of the limitations associated with the method of data collection.
Such a sparse amount of data leads to an indistinguishability problem between the various proposed models (ie biologically different models can describe the same data set equally well).
Only a sparse amount of data is available concerning the initial CPR success rates and long-term survival in adult ICUs from Germany.
To compensate for this imbalance and create a more homogeneous classification, exposure levels with sparse presence of data should be represented by a wide interval of density estimation and vice versa.
