Efficiently projects vectors onto top PCA components without explicit PCA.
problem Efficiently project vectors onto top principal components of a matrix.
method Iterative algorithm using ridge regression and polynomial approximation.
result First runtime improvement for principal component regression.
Polynomial chaos expansions on Grassmannian submanifolds for high-dimensional stochastic systems.
problem Uncertainty quantification in high-dimensional stochastic systems.
method Principal Geodesic Analysis on the Grassmann manifold, adaptive algorithm for local submanifolds, polynomial chaos expansion.
result Efficient surrogate modeling of system behavior across different parameter spaces.
Study uses big data to analyze quantum invariants.
problem Investigate structural properties of Jones polynomial.
method Exploratory and topological data analysis, including coloring, rank increase, categorification.
result Contrasts behavior of Jones polynomial under various enhancements.
New method analyzes Jones polynomial manifold structure.
problem Understanding the structure of Jones polynomial.
method Filtrations and Principal Component Analysis for infinite data sets.
result Jones polynomial can be viewed as an approximately 3 dimensional manifold.
PPA models data directions as curves for manifold learning.
problem Nonlinear data structure in manifold learning.
method Principal Polynomial Analysis (PPA) models data directions as curves.
result PPA reduces to simple univariate regressions, making it computationally feasible and robust.
Solves PCR with fewer calls to ridge regression.
problem Principal component regression (PCR) with high accuracy.
method Reduces PCR to ridge regression calls and develops stable recurrence for matrix Chebyshev polynomials.
result Achieves PCR with multiplicative accuracy up to 1+γ using fewer calls. New method for sparse PCA using random projections, non-iterative and fast.
problem Sparse principal component analysis (PCA)
method Axis-aligned random projections of sample covariance matrix
result Non-iterative method achieves optimal convergence rate in polynomial time
We perform a finite sample analysis of the detection levels for sparse principal components of a high-dimensional covariance matrix. Our minimax optimal test is based on a sparse eigenvalue statistic. Alas, computing this test is known to be NP-complete in general, and we describe a computationally efficient alternativ…
UPCA solves data matrix completion with permuted columns.
problem Data matrix completion with permuted columns.
method Algebraic geometry and two-stage algorithm.
result UPCA recovers the ground-truth matrix from corrupted data.
Paper corrects a proof about biharmonic hypersurfaces with three distinct curvatures.
problem Proving constant mean curvature for biharmonic hypersurfaces with three distinct principal curvatures.
method Analyzing the resultant of polynomials to identify a special case.
result In the special case, the hypersurface still has constant mean curvature.
Fast algorithm recovers principal eigenvector from noisy matrices.
problem Recovering the first principal eigenvector from noisy positive semidefinite matrices.
method Cone projected power iteration algorithm.
result Achieves polynomial time complexity and small error for certain convex cones.
Principal components analysis (PCA) is the optimal linear auto-encoder of data, and it is often used to construct features. Enforcing sparsity on the principal components can promote better generalization, while improving the interpretability of the features. We study the problem of constructing optimal sparse linear a…
FPCA optimizes fairness in target vectors' span.
problem Fairness in principal component analysis for multiple target vectors.
method Non-concave maximization of worst projected target norm using sub-gradient descent.
result Optimization landscape is benign with globally optimal local minima.
Habiro gave principal ideals of Z[q,q^{-1}] in which certain linear combinations of the colored Jones polynomials of algebraically-split links take values. The author proved that the same linear combinations for ribbon links, boundary links and Brunnian links are contained in smaller ideals of Z[q,q^{-1}] generated by …
The computation of the sparse principal component of a matrix is equivalent to the identification of its principal submatrix with the largest maximum eigenvalue. Finding this optimal submatrix is what renders the problem NP-hard. In this work, we prove that, if the matrix is positive semidefinite and its …
SPCA extracts nonlinear features for feature extraction.
problem Nonlinear feature extraction in data.
method Unsupervised, nonlinear, invertible feature extraction technique.
result Identifies curvilinear features interpretable as nonlinear sensors.
Formula for umbilic points on polynomial surfaces, proving their isolated nature and topological type.
problem Understanding the global behavior of fields of principal directions on polynomial surfaces.
method Poincaré-Hopf type formula and projective extension analysis.
result Every umbilic point at infinity has index 1/2 and topological type a Lemon.
Researchers compute differential K-theory for moduli stacks.
problem Computing differential K-theory for moduli stacks of principal G-bundles.
method Using homotopy theory of presheaves of spaces and spectra, they formulate results in terms of invariant polynomials and representation rings.
result They successfully compute the connective differential K-theory and differential cohomology of moduli stacks.
Study a volume preserving flow using symmetric polynomials without curvature pinching assumptions.
problem Volume preserving flow of convex hypersurfaces without curvature pinching constraints.
method Power of the k-th elementary symmetric polynomial in principal curvatures.
result Solution exists for all times and converges to a round sphere in the volume preserving scalar curvature flow case.
In recent years, sparse principal component analysis has emerged as an extremely popular dimension reduction technique for high-dimensional data. The theoretical challenge, in the simplest case, is to estimate the leading eigenvector of a population covariance matrix under the assumption that this eigenvector is sparse…
The paper connects curvature data to polynomial coefficients in gluing formulas.
problem Understanding polynomial coefficients in gluing formulas for zeta-determinants.
method Expressing coefficients of a polynomial in terms of scalar and principal curvatures of a 2D hypersurface.
result Coefficients of the polynomial are expressed in terms of curvature data.
Differential quantities, including normals, curvatures, principal directions, and associated matrices, play a fundamental role in geometric processing and physics-based modeling. Computing these differential quantities consistently on surface meshes is important and challenging, and some existing methods often produce …
Study on liquidity dynamics in Uniswap v3 pools using statistical methods.
problem Characterize liquidity in Uniswap v3 pools.
method Functional principal component analysis (FPCA) and dynamic factor methods.
result Liquidity dynamics in Uniswap v3 pools are well-captured by a low-order Legendre polynomial basis.
Efficient private matrix analysis algorithms for recent variants.
problem Private analysis of recent matrix updates.
method Identifying sufficient conditions on positive semidefinite matrices.
result First efficient differentially private algorithms for various matrix analysis tasks.
Fine-grained analysis of gradient descent with momentum provides modified loss equations.
problem Understanding the dynamics of gradient descent with momentum.
method Fine-grained analysis and derivation of modified loss equations.
result Global approximation bounds and continuous modified equations for HB.
Spofe bridges statistical rigor and interpretability in feature extraction from tabular data.
problem Ensuring statistical rigor and interpretability in feature extraction from complex tabular data.
method Spofe combines kernel principal components and sparse polynomial functions with a multi-objective knockoff selection procedure.
result Spofe consistently outperforms other methods in feature selection for regression and classification tasks.
The paper introduces a method for interpretable principal component analysis of high-dimensional time series.
problem Inconsistent and difficult-to-interpret principal component estimates in high-dimensional regimes.
method Localized sparse principal component analysis of spectral density matrices in frequency domain.
result Efficient algorithm for sparse-localized estimates of principal subspaces.
New unsupervised method for dimensionality reduction via regression in hyperspectral imagery.
problem Collinearity and ill-determination problems in high-dimensional spectral data.
method DRR (Dimensionality Reduction via Regression) using multivariate regression.
result DRR outperforms linear PCA and other nonlinear methods in reducing dimensionality and improving classification accuracy.
It is well known that Sparse PCA (Sparse Principal Component Analysis) is NP-hard to solve exactly on worst-case instances. What is the complexity of solving Sparse PCA approximately? Our contributions include: 1) a simple and efficient algorithm that achieves an n−1/3-approximation; 2) NP-hardness of approximatio…
Generalizes PCA to maximize any convex function of components.
problem Finding a principal vector that maximizes a convex function of components.
method Gradient ascent algorithm for solving the generalized PCA problem; fixed points of neural networks for kernel version.
result Solutions can be obtained as fixed points of simple neural networks.
Two new PCA variants improve financial data analysis.
problem Numerical instability and nonstationarity in PCA for finance.
method Iterated and exponentially weighted moving PCA variants using Ogita-Aishima iteration.
result Improved stability and adaptability in financial data analysis.
In this dissertation, the main goal is visualisation of financial time series. We expect that visualisation of financial time series will be a useful auxiliary for technical analysis. Firstly, we review the technical analysis methods and test our trading rules, which are built by the essential concepts of technical ana…
We study sparse principal component analysis for high dimensional vector autoregressive time series under a doubly asymptotic framework, which allows the dimension d to scale with the series length T. We treat the transition matrix of time series as a nuisance parameter and directly apply sparse principal component…
Analyzes last few principal components for stock correlations.
problem Identifying highly correlated stocks for better portfolio management.
method Principal component analysis of correlation matrix.
result Last few components contain useful financial information.
Paper uses PCA to analyze Chinese sovereign bonds and discusses bond immunization.
problem Analyzing factors affecting Chinese sovereign bond yield changes.
method Applied Principal Component Analysis (PCA) on bond yield data.
result Identified principal factors influencing Chinese sovereign bond yield changes.
Modularity component analysis clusters data without centering.
problem Clustering data without centering.
method Developed exact linear relation between modularity matrix eigenvectors and singular vectors.
result Modularity component analysis clusters data similarly to PCA but without centering.
Paper presents a faster classical algorithm for principal component regression.
problem Efficiently solving principal component regression problems.
method Uses quantum-inspired linear algebra techniques.
result Achieves polylogarithmic runtime, significantly faster than state-of-the-art.
Paper introduces MPPGA for integrating multiple PGA models on Riemannian manifolds.
problem Challenges in dimensionality reduction on Riemannian manifolds with multiple modalities.
method Develops a mixture probabilistic principal geodesic analysis (MPPGA) model.
result Demonstrates improved clustering and shape analysis using MPPGA.
Riemannian stochastic gradient descent converges faster with increasing batch size.
problem Improving convergence rate of Riemannian stochastic gradient descent.
method Theoretical analysis and numerical investigation of increasing batch size effects.
result Riemannian stochastic gradient descent converges faster with increasing batch size.
Polynomial algorithm for multiplication on one-hole torus skein algebra.
problem Complexity of multiplicative structure in skein algebra.
method Provided a polynomial algorithm for one-hole torus.
result Closed form formulas for multiplication of curves with low crossing number.
New local-search methods close the gap in sparse tensor PCA.
problem Sparse tensor PCA underperforms compared to other methods.
method Proposes new local-search methods including greedy and random-threshold variants.
result Proves local-search methods close the gap to best known polynomial-time procedures.
Two new algorithms improve robust PCA and Schatten packing.
problem Robustly estimating the top eigenvector of corrupted sub-Gaussian data.
method Two iterative filtering and nearly-linear time algorithms.
result First polynomial-time algorithms for non-trivial covariance estimation.
The paper uses diffusion approximations to analyze and optimize online principal component estimation.
problem Optimizing online principal component estimation from streaming data.
method Diffusion approximation tools applied to Oja's iteration for principal component analysis.
result The Oja's iteration for the top eigenvector generates a continuous-state discrete-time Markov chain over the unit sphere.
R-PCA extends PCA to Riemannian manifolds for structured data.
problem Applying PCA to data on Riemannian manifolds without vector space operations.
method Adapting PCA to Riemannian manifolds by equipping data with local metrics.
result Unified approach for dimensionality reduction and statistical analysis on manifolds.
Essential principal components simplify spectral analysis with minimal training data.
problem Accurate spectral quantification from complex mixtures.
method Identifying essential principal components and using molar extinction coefficients.
result Near one-to-one projection from principal components to mixture constituents.
Study explores K-means clustering of variables and its relation to PCA.
problem Exploring the relationship between K-means clustering of variables and PCA.
method Apply PCA to original data and K-means to transposed data, quantify variable contributions to principal components.
result Identifies how variable clusters contribute to principal components identified by PCA.
TensorSketch solves Kronecker product regression and non-negative regression.
problem Solving regression problems with Kronecker product matrices.
method Extending TensorSketch to other norms for Kronecker product regression.
result Solving Kronecker product regression and non-negative regression in sublinear time.
QAPCA uses quantum annealing for robust PCA.
problem Outliers in data skew L2-norm principal components.
method Quantum annealing for L1-norm optimization.
result QAPCA's reconstruction error is comparable to L1-BF.