On improving robustness of LDA and SRDA by using tangent vectors
We present approaches to include tangent vector information in LDA and SRDA while retaining their computational cost. This provides a way of taking better advantage of the limited available data, possibly avoiding singularity problems. In experimental results, the recognition performance either is unaffected or it improves. The methods become more robust to the known transformations used during learning.
lda srda tangent vectors subspace learning dimensionality reduction