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.
Proposes joint LCA for multiview data to identify shared and view-specific components.
QAPCA uses quantum annealing for robust PCA.
Independent component analysis (ICA) decomposes multivariate data into mutually independent components (ICs). The ICA model is subject to a constraint that at most one of these components is Gaussian, which is required for model identifiability. Linear non-Gaussian component analysis (LNGCA) generalizes the ICA model t…
Improved estimation of multiple principal components using manifold optimization and iterative deflation techniques.
We present a new method for the separation of superimposed, independent, auto-correlated components from noisy multi-channel measurement. The presented method simultaneously reconstructs and separates the components, taking all channels into account and thereby increases the effective signal-to-noise ratio considerably…
New FGSPCA method captures grouping and sparse structures in PCA without prior info.
The statistical dependencies which independent component analysis (ICA) cannot remove often provide rich information beyond the linear independent components. It would thus be very useful to estimate the dependency structure from data. While such models have been proposed, they usually concentrated on higher-order corr…
This paper is concerned with an important issue in finite mixture modelling, the selection of the number of mixing components. We propose a new penalized likelihood method for model selection of finite multivariate Gaussian mixture models. The proposed method is shown to be statistically consistent in determining of th…
A new neural network reduces high-dimensional time-series data for faster classification.
We develop a mean-field theory for multi-component ICA in high dimensions.
Study flip graphs for surfaces of infinite type, finding uncountably many connected components.
A new method for sparse PCA using orthogonal rotations and soft-thresholding.
Tensor CANDECOMP/PARAFAC (CP) decomposition is an important tool that solves a wide class of machine learning problems. Existing popular approaches recover components one by one, not necessarily in the order of larger components first. Recently developed simultaneous power method obtains only a high probability recover…
Study uses geometric algebra to analyze credit cycles, revealing dangerous feedback loops.
This paper solves tensor robust principal component analysis via scaled gradient descent.
New method disentangles hidden data structures using HSIC and supervision.
Direct contextual policy search methods learn to improve policy parameters and simultaneously generalize these parameters to different context or task variables. However, learning from high-dimensional context variables, such as camera images, is still a prominent problem in many real-world tasks. A naive application o…
Two new models forecast multiple subpopulations' mortality, outperforming existing methods.
ML PCA detects phase transitions in muon spectroscopy data.
Paper combines geometry and time-series analysis for spatiotemporal data.
Revisits PCA with new formulations and insights.
The paper develops methods to accurately locate change points in high-dimensional mean shift models.
A method for representing and comparing categorical trajectories using multivariate functional principal components.
Proposes ESCA model to analyze mixed data types in multiple sets of measurements.
Principal component regression (PCR) is a widely used two-stage procedure: principal component analysis (PCA), followed by regression in which the selected principal components are regarded as new explanatory variables in the model. Note that PCA is based only on the explanatory variables, so the principal components a…
Paper estimates GMMs with unknown covariances using sparse regularization.
SDR outperforms IDR in multimodal data analysis, especially with fewer samples.
Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well known techniques in da…
Proposes a new model for high-dimensional data analysis with unknown link function.
We prove the existence of a degree 7 Vassiliev invariant of long (or string) two-component links which is not preserved under the simultaneous change of orientation of both components. The non-invertibility of this invariant can be detected by the standard weight system with values in the tensor square of the universal…
Principal component regression (PCR) is a two-stage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variables. Note that the principal components are obtained from only explanatory variables and not considered with the response variable. To addre…
Given a 3-manifold M containing an incompressible surface Q, we obtain an inequality relating the Heegaard genus of M and the Heegaard genera of the components of M - Q. Here the sum of the genera of the components of M - Q is bounded above by a linear expression in terms of the genus of M, the Euler characteristic of …
In several application domains, high-dimensional observations are collected and then analysed in search for naturally occurring data clusters which might provide further insights about the nature of the problem. In this paper we describe a new approach for partitioning such high-dimensional data. Our assumption is that…
We consider the problem of learning a mixture of Random Utility Models (RUMs). Despite the success of RUMs in various domains and the versatility of mixture RUMs to capture the heterogeneity in preferences, there has been only limited progress in learning a mixture of RUMs from partial data such as pairwise comparisons…
We develop a coherent framework for integrative simultaneous analysis of the exploration-exploitation and model order selection trade-offs. We improve over our preceding results on the same subject (Seldin et al., 2011) by combining PAC-Bayesian analysis with Bernstein-type inequality for martingales. Such a combinatio…
A deep learning framework for survival analysis combining piecewise exponential models.
In neuroimaging data analysis, Gaussian graphical models are often used to model statistical dependencies across spatially remote brain regions known as functional connectivity. Typically, data is collected across a cohort of subjects and the scientific objectives consist of estimating population and subject-specific g…
New method for semiparametric bandits reduces regret to optimal levels.
ProJIVE integrates multiple data types to explain joint and individual variation.
The paper solves the problem of fitting an ellipsoid to random points efficiently.
PCA improves detection of phase transitions in muon spectroscopy data from various materials.
NMF identifies hidden component processes from thermal manufacturing data.
New algorithms improve tensor CP decomposition under mild conditions.
We investigate how simultaneously recorded long-range power-law correlated multi-variate signals cross-correlate. To this end we introduce a two-component ARFIMA stochastic process and a two-component FIARCH process to generate coupled fractal signals with long-range power-law correlations which are at the same time lo…
We consider the task of classification in the high dimensional setting where the number of features of the given data is significantly greater than the number of observations. To accomplish this task, we propose a heuristic, called sparse zero-variance discriminant analysis (SZVD), for simultaneously performing linear …
DPA autoencoders learn data distribution and intrinsic dimensionality with guarantees.
New method estimates causal structure from sparse data.
New PCA method handles multiple datasets and detects sparse patterns robustly.