Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition

Algorithm 1

Framework of Cross-Dataset Feature Alignment and Generalization (CDFAG).

Input:

Raw features , the number of input data domains , dimension

of common latent subspace , trade-off parameter , maximum iterations 1000, input layer size L, output layer size

L, hidden layer size H, learning rate 0.1, momentum 0.9 and are randomly initialized.

Feature alignment:

(1)Map the raw features from datasets to Hilbert spaces:

(2)Construct graph Laplacian matrices , and defined in Section 3.2.2.

(3)Compute the mapping functions by finding the smallest eigenvalues of the generalized eigenvalue problem:

(4)Apply to map input datasets to the new dimensional common latent space to obtain aligned features:

Feature generalization:

(5)Calculate the target outputs of aligned-to-generalized encoders from class c:

,

and denote the aligned features of th and th training instances from class c in the source and target dataset.

(6)for iter = 1 to 1000 do

(7)Minimize objective function:

for both encoders in parallel via stochastic gradient descent.

(8)end for

(9)Take the activations of aligned-to-generalized encoders as the final generalized features.

Output:

Generalized features across different datasets.