A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This study examines whether PCA can effectively identify nitrogen pollution sources in rivers.
A new portfolio method using quantum mechanics improves risk diversification.
Principal Components Regression (PCR) is a traditional tool for dimension reduction in linear regression that has been both criticized and defended. One concern about PCR is that obtaining the leading principal components tends to be computationally demanding for large data sets. While random projections do not possess…
A new method reduces data movement in neural network training.
Methods for analysis of principal components in discrete data have existed for some time under various names such as grade of membership modelling, probabilistic latent semantic analysis, and genotype inference with admixture. In this paper we explore a number of extensions to the common theory, and present some applic…
HeMPPCAT improves PCA for data with varying noise.
FCPCA fuzzy clusters high-dimensional time series data efficiently.
Efficient private matrix analysis algorithms for recent variants.
Tensor completion and robust principal component analysis have been widely used in machine learning while the key problem relies on the minimization of a tensor rank that is very challenging. A common way to tackle this difficulty is to approximate the tensor rank with the norm of singular values based on its …
The paper introduces a method for interpretable principal component analysis of high-dimensional time series.
Principal Component Analysis (PCA) is the most common nonparametric method for estimating the volatility structure of Gaussian interest rate models. One major difficulty in the estimation of these models is the fact that forward rate curves are not directly observable from the market so that non-trivial observational e…
Two new models forecast multiple subpopulations' mortality, outperforming existing methods.
We present a large-scale study of commonality in liquidity and resilience across assets in an ultra high-frequency (millisecond-timestamped) Limit Order Book (LOB) dataset from a pan-European electronic equity trading facility. We first show that extant work in quantifying liquidity commonality through the degree of ex…
Two new PCA variants improve financial data analysis.
New simulations advise caution in choosing principal components for multivariate functional data.
Essential principal components simplify spectral analysis with minimal training data.
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…
The paper tackles noisy functional data by exploring a multivariate perspective.
Conventional principal component analysis (PCA) finds a principal vector that maximizes the sum of second powers of principal components. We consider a generalized PCA that aims at maximizing the sum of an arbitrary convex function of principal components. We present a gradient ascent algorithm to solve the problem. Fo…
QAPCA uses quantum annealing for robust PCA.
We study sparse principal component analysis for high dimensional vector autoregressive time series under a doubly asymptotic framework, which allows the dimension to scale with the series length . We treat the transition matrix of time series as a nuisance parameter and directly apply sparse principal component…
In this paper the exact linear relation between the leading eigenvectors of the modularity matrix and the singular vectors of an uncentered data matrix is developed. Based on this analysis the concept of a modularity component is defined, and its properties are developed. It is shown that modularity component analysis …
A new robust PCA estimator combining M-estimators and minimum divergence estimators.
In this work we propose a method for reducing the dimensionality of tensor objects in a binary classification framework. The proposed Common Mode Patterns method takes into consideration the labels' information, and ensures that tensor objects that belong to different classes do not share common features after the redu…
The excluded area between a pair of two-dimensional hard particles with given relative orientation is the region in which one particle cannot be located due to the presence of the other particle. The magnitude of the excluded area as a function of the relative particle orientation plays a major role in the determinatio…
GT-PCA improves PCA for image and time series data.
This paper analyses the Chinese Sovereign bond yield to find out the principal factors affecting the term structure of interest rate changes. We apply Principal Component Analysis (PCA) on our data consisting of the Chinese Sovereign bond from January 2002 till May 2018 with the different yield to maturity. Then we wil…
Study explores K-means clustering of variables and its relation to PCA.
Paper develops a dual formulation for PCA in Hilbert spaces.
The paper uses PCA and HMM to forecast stock returns outperforming buy-and-hold.
We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression. By avoiding explicit principal …
A new method uses Gram matrix for efficient multivariate functional principal components.
New method embeds correlation networks to reveal underlying time series patterns.
Principal component analysis (PCA) is recognised as a quintessential data analysis technique when it comes to describing linear relationships between the features of a dataset. However, the well-known sensitivity of PCA to non-Gaussian samples and/or outliers often makes it unreliable in practice. To this end, a robust…
Proposes an online method for high-dimensional streaming data.
Two derivations of PCA for distributional data.
Improved convergence speed of principal component analysis through modified learning rules.
Principal component analysis (PCA) has been a prominent tool for high-dimensional data analysis. Online algorithms that estimate the principal component by processing streaming data are of tremendous practical and theoretical interests. Despite its rich applications, theoretical convergence analysis remains largely ope…
We propose a fair principal component analysis method that balances reconstruction error and subgroup fairness.
R-PCA extends PCA to Riemannian manifolds for structured data.
The paper tackles CF in CL by analyzing NTK overlap matrix and proposing OGD.
In this paper, we propose to adopt the diffusion approximation tools to study the dynamics of Oja's iteration which is an online stochastic gradient descent method for the principal component analysis. Oja's iteration maintains a running estimate of the true principal component from streaming data and enjoys less tempo…
Modern techniques simplify complex high-dimensional data.
New method improves PCA for high-dimensional data with n < p.
Generalized principal component analysis (GLM-PCA) facilitates dimension reduction of non-normally distributed data. We provide a detailed derivation of GLM-PCA with a focus on optimization. We also demonstrate how to incorporate covariates, and suggest post-processing transformations to improve interpretability of lat…
Proposes MPCA for robust PCA using mode estimation.
Regularized MFPCA smooths multivariate functional data for clearer patterns.
Outlier based Robust Principal Component Analysis (RPCA) requires centering of the non-outliers. We show a "bias trick" that automatically centers these non-outliers. Using this bias trick we obtain the first RPCA algorithm that is optimal with respect to centering.