Spectral methods for data science: A statistical perspective
Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
Optimality of spectral clustering in the Gaussian mixture model
In the Supplementary Material [42], we first present some propositions that characterize the
population quantities in Appendix A. Then in Appendix B, we give several auxiliary lemmas …
population quantities in Appendix A. Then in Appendix B, we give several auxiliary lemmas …
An theory of PCA and spectral clustering
An lp theory of PCA and spectral clustering Page 1 The Annals of Statistics 2022, Vol. 50, No.
4, 2359–2385 https://doi.org/10.1214/22-AOS2196 © Institute of Mathematical Statistics, 2022 …
4, 2359–2385 https://doi.org/10.1214/22-AOS2196 © Institute of Mathematical Statistics, 2022 …
Entrywise estimation of singular vectors of low-rank matrices with heteroskedasticity and dependence
J Agterberg, Z Lubberts… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
We propose an estimator for the singular vectors of high-dimensional low-rank matrices
corrupted by additive subgaussian noise, where the noise matrix is allowed to have …
corrupted by additive subgaussian noise, where the noise matrix is allowed to have …
Leave-one-out singular subspace perturbation analysis for spectral clustering
In the supplement [46], we first provide the proof of Theorem 2.3 in Appendix A, followed by
the proofs of results of Section 3.4 in Appendix B. The proof of Theorem 3.3 is given in …
the proofs of results of Section 3.4 in Appendix B. The proof of Theorem 3.3 is given in …
Reconciling business analytics with graphically initialized subspace clustering for optimal nonlinear pricing
The relationship between price and quantity in nonlinear pricing transcends simple
proportionality, as conditional rebates and discounts may be contingent upon the quantity of …
proportionality, as conditional rebates and discounts may be contingent upon the quantity of …
Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA
This paper is concerned with estimating the column subspace of a low-rank matrix
$\boldsymbol {X}^\star\in\mathbb {R}^{n_1\times n_2} $ from contaminated data. How to …
$\boldsymbol {X}^\star\in\mathbb {R}^{n_1\times n_2} $ from contaminated data. How to …
Molecular docking aided machine learning for the identification of potential VEGFR inhibitors against renal cell carcinoma
VS Jerra, B Ramachandran, S Shareef… - Medical Oncology, 2024 - Springer
Renal cell carcinoma is a highly vascular tumor associated with vascular endothelial growth
factor (VEGF) expression. The Vascular Endothelial Growth Factor-2 (VEGF-2) and its …
factor (VEGF) expression. The Vascular Endothelial Growth Factor-2 (VEGF-2) and its …
On data-driven prescriptive analytics with side information: A regularized nadaraya-watson approach
We consider generic stochastic optimization problems in the presence of side information
which enables a more insightful decision. The side information constitutes observable …
which enables a more insightful decision. The side information constitutes observable …
Robust spectral clustering with rank statistics
This paper analyzes the statistical performance of a robust spectral clustering method for
latent structure recovery in noisy data matrices. We consider eigenvector-based clustering …
latent structure recovery in noisy data matrices. We consider eigenvector-based clustering …