Phase transition conjecture for PCA subspace and score recovery

Let n,dn,d\to\infty with n/dc>0n/d\to c>0 and n/npn_\ell/n\to p_\ell for {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}2a.s.0,z^(i)n,Span{z(1),,z(k)}2a.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.

Sources & referencesView supporting material

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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