The sparsity constraint can be implemented in various ways: The overall loss function for training a sparse autoencoder includes the reconstruction loss and the sparsity penalty: Lₜₒₜₐₗ = L( x, x̂ ) + ...

Instead, the activations within a given layer are penalized, setting it up so the loss function better captures the statistical features of input data. To put that another way, while the hidden layers ...

Minimizes the loss function between the output node and the corrupted input. Have hidden nodes greater than input nodes. They can still discover important features from the data. A generic sparse ...

In addition to the traditional autoencoder reconstruction-based loss and L2 regularization loss, we implement two additional constraints in the loss function to encourage ... We can encode inputs as ...

We first plot the loss function curves for miRNAs and diseases latent features based on different dimension obtained through the sparse autoencoder, respectively, as shown in Figure 2. The curve loss ...

Abstract: The training performance of an autoencoder is significantly affected by its loss function. In order to improve the performance of autoencoders, it is important to design ap-propriate loss ...

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Next, the obtained feature vectors are mapped to a low-dimensional space based on a Sparse AutoEncoder. Finally, unknown microbe-disease pairs are classified based on Light Gradient boosting machine.