- What would happen to the US Economy if taxes exceed 20% of GDP
- What is the economic incentive to cheat? How does an experiment capture exogenous deviations?
- Can my character have a pet mammoth?
- Where is the mention of Surya Loka(सूर्य लोकं) in Hinduism?
- Mysql_close ()-why few people apply
- 25x25 slitherlink puzzle
- Is there a quick way to speed up ICP in python using a cached KD-tree
- HC 05, Arduino Uno
- Remove lag between PS3 input via bluetooth to arduino
- Use esp32 as a secure sockets
- Best way to organize many pdf's?
- Is EPUB ready for most phones?
- Where is the fuel stored on an aircraft’s wing?
- What are the aileron lengths of commercial and military aircraft?
- Do jet aircraft have an emergency propeller?
- Are there different configurations of cabin crew seating arrangement for one aircraft model?
- How do PPL, CPL, and ATPL compare?
- To what extent is remuneration under a PPL enforced in the UK?
- What criteria are used for exiting an airplane in an emergency?
- Getting my dog ready to Adopt a new cat

# How to obtain covariance matrix eigenvalues from singular values?

I would like to implement closed form of PPCA (Bishop, Tipping, 1999, Appendix A). In this paper they calculate $W$ in formula (15):

$W=U_q(K_q-\sigma^2I)^{1/2}R$

where $K_q$ is a matrix from eigenvalues $\lambda_i$ of covariance matrix as defined in formula (5).

On one hand I read here, that it is possible to calculate eigenvalues with

$\lambda_i = \frac{s_i^2}{n-1}$

Is it true, that principal values depend on number of samples $n$?

On other hand I found python implementation of closed PPCA on github, function __fit_ml, where it is written

mu = np.mean(self.y, 1)[:, np.newaxis]

[u, s, v] = np.linalg.svd(self.y - mu)

...

else:

ss = s[:self.q]

ss = np.sqrt(np.maximum(0, ss**2 - self.prior_sigma))

w = u[:, :self.q].dot(np.diag(ss))

where author is apparently calculating with

$\lambda_i = s_i^2$

which is completely different.

UPDATE

Here $s_i$ is diagonal element of singular values matrix.