Phase-transition conjecture for support vector proliferation in ℓ₁-SVMs
Let be an isotropic Gaussian sample, and let SVP denote support vector proliferation, namely coincidence between the solutions of the corresponding SVM and minimum--norm interpolation problems. Phase-transition conjecture for -SVMs. The probability of SVP occurring for an -SVM with and undergoes a phase transition around , for some . Formally, there exist positive constants and with such that
The conjecture predicts that support vector proliferation for -SVMs requires a substantially higher-dimensional regime than the corresponding phenomenon for -SVMs; the precise growth rate of 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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