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Optimising the LR + SI Covariance Model via EM/RCA ◮ Initialise σ 2 , W and Λ. ◮ REPEAT ◮ E-step: Update posterior density p(Z|Y) given current Λ.
Optimising the LR + SI Covariance Model via EM/RCA ◮ Initialise σ 2 , W and Λ. ◮ REPEAT ◮ E-step: Update posterior density p(Z|Y) given current Λ. ◮ M-step: Update Λ by GLASSO optimisation on the expected complete data log-density (expectation wrt current posterior p(Z|Y)).
Page 1 and 2: Residual Component Analysis: Genera
Page 3 and 4: Probabilistic PCA (sensible PCA) (H
Page 5 and 6: Dual PPCA (principal Coordinate ana
Page 7 and 8: From dual-PPCA to the GPLVM (Lawren
Page 9 and 10: From dual-PPCA to the GPLVM (Lawren
Page 11 and 12: Residual Component Analysis ◮ Dis
Page 21 and 22: Residual Component Analysis (Kalait
Page 23 and 24: Low-rank + Sparse-Inverse Covarianc
Page 25: Optimising the LR + SI Covariance M
Page 29 and 30: Simulations Precision 1 0.9 0.8 0.7
Page 31 and 32: Reconstructing the Human Form y 0.2
Page 33 and 34: Reconstructing the Human Form Confo
Page 35: Reconstruction of a protein-signall

Residual Component Analysis: Generalising PCA for more flexible ...

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