Phase-transition conjecture for support vector proliferation in ℓ₁-SVMs

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Let (\bX,y)∈Rn×d×Rn(\bX,y) \in \mathbb{R}^{n \times d} \times \mathbb{R}^n be an isotropic Gaussian sample, and let SVP denote support vector proliferation, namely coincidence between the solutions of the corresponding SVM and minimum-ℓ1\ell_1-norm interpolation problems. Phase-transition conjecture for ℓ1\ell_1-SVMs. The probability of SVP occurring for an ℓ1\ell_1-SVM with \bX\bX and yy undergoes a phase transition around d=f(n)d=f(n), for some f(n)=ω(nlog⁡n)f(n)=\omega(n\log n). Formally, there exist positive constants cc and c′c' with c≤c′c\leq c' such that

lim⁡n→∞P(SVP occurs for an ℓ1-SVM)={0if d<cf(n),1if d>c′f(n).\lim_{n\to\infty}\mathbb{P}(\text{SVP occurs for an $\ell_1$-SVM})=\begin{cases}0&\text{if }d<cf(n),\\1&\text{if }d>c'f(n). \end{cases}

The conjecture predicts that support vector proliferation for ℓ1\ell_1-SVMs requires a substantially higher-dimensional regime than the corresponding phenomenon for ℓ2\ell_2-SVMs; the precise growth rate of f(n)f(n) is not established by the conjecture.

References

Primary source

Navid Ardeshir, Clayton Sanford and Daniel Hsu, “Support vector machines and linear regression coincide with very high-dimensional features”, arXiv:2105.14084 (2021).

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