Hence, sin2 θ i, where θ i is the angle a vector makes with an independent i-dimensional subspace, is distributed as F sin 2 θ i ( M ; x ) as detailed in Appendix B. For the above expression, encoding order is K s to 1.
This is distributed as is.
In general, this subspace is not an eigenspace of the measurement operator or the Hamiltonian.
Therefore, the dynamics induced by projects the system into some subspace, but since this does not happen via any of the mechanisms described above it is not immediately obvious what this subspace is.
This subspace is, by definition, the tangent space TPM.
By taking the center of each class as the base vector structure subspace (CI subspace), and then mapping each text vector to this subspace, the representation of text vectors to this subspace is acquired.
This subspace is represented by latent variables called score- and loading vectors.
The dimension of this subspace is the deficiency of the network, δ= d i m(D δ ).
Mix this well so everything is distributed.
In such cases, it can often be assumed that the target data is distributed in subspaces of much lower dimensionality.
