Phase transition conjecture for PCA subspace and score recovery

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Let n,d→∞n,d\to\infty with n/d→c>0n/d\to c>0 and nℓ/n→pℓn_\ell/n\to p_\ell for ℓ∈{1,…,L}\ell\in\{1,\dots,L\}. Let AA and βi\beta_i be the quantities defined in the paper, and let u^i\hat{u}_i be the iith principal component and z^(i)/n\hat{z}^{(i)}/\sqrt n the corresponding normalized score vector. Phase transition conjecture. If A(βi)≤0A(\beta_i)\leq 0, then

∣⟨u^i,Span⁡{u1,…,uk}⟩∣2⟶a.s.0,∣⟨z^(i)n,Span⁡{z(1),…,z(k)}⟩∣2⟶a.s.0.\left|\left\langle \hat{u}_i,\operatorname*{Span}\{u_1,\dots,u_k\}\right\rangle\right|^2\overset{a.s.}{\longrightarrow}0, \qquad \left|\left\langle \frac{\hat{z}^{(i)}}{\sqrt n},\operatorname*{Span}\{z^{(1)},\dots,z^{(k)}\}\right\rangle\right|^2\overset{a.s.}{\longrightarrow}0.

This predicts that both principal-component and score-vector recovery vanish at the phase transition A(βi)=0A(\beta_i)=0, extending the positive-recovery formulas to the regime where A(βi)≤0A(\beta_i)\leq0.

References

Primary source

David Hong, Laura Balzano and Jeffrey A. Fessler, “Asymptotic performance of PCA for high-dimensional heteroscedastic data”, arXiv:1703.06610 (2018).

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