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.
Minimalistic unsupervised learning with sparse manifold transform achieves SOTA performance.
Gradient-based method extracts slow features from high-dimensional data.
We develop a 2D travel time tomography method which regularizes the inversion by modeling groups of slowness pixels from discrete slowness maps, called patches, as sparse linear combinations of atoms from a dictionary. We propose to use dictionary learning during the inversion to adapt dictionaries to specific slowness…
We present a signal representation framework called the sparse manifold transform that combines key ideas from sparse coding, manifold learning, and slow feature analysis. It turns non-linear transformations in the primary sensory signal space into linear interpolations in a representational embedding space while maint…
Derives a biologically plausible neural network for Slow Feature Analysis.
This work interprets SFA through variational inference, relaxing linearity constraints.
Paper shows how SFA fits into FBM framework for time series separation.
Proposes a sparse Naïve Bayes classifier to improve performance and interpretability.
Paper proposes a method to monitor industrial processes under closed-loop control.
Study reveals conditions for neural networks to forget learned features.
Stochastic gradient descent (SGD) is commonly used for optimization in large-scale machine learning problems. Langford et al. (2009) introduce a sparse online learning method to induce sparsity via truncated gradient. With high-dimensional sparse data, however, the method suffers from slow convergence and high variance…
Slow feature analysis (SFA) is an unsupervised-learning algorithm that extracts slowly varying features from a multi-dimensional time series. A supervised extension to SFA for classification and regression is graph-based SFA (GSFA). GSFA is based on the preservation of similarities, which are specified by a graph struc…
Generalized linear model with and regularization is a widely used technique for solving classification, class probability estimation and regression problems. With the numbers of both features and examples growing rapidly in the fields like text mining and clickstream data analysis parallelization and the us…
New Bayesian method for sparse multidimensional item response theory.
Robust ASR model removes fast-changing features to resist attacks.
Extended Predictable Feature Analysis (PFAx) [Richthofer and Wiskott, 2017] is an extension of PFA [Richthofer and Wiskott, 2015] that allows generating a goal-directed control signal of an agent whose dynamics has previously been learned during a training phase in an unsupervised manner. PFAx hardly requires assumptio…
New method uses sparse random features for crashworthiness analysis.
Bayesian method for feature selection with grouping info using expectation propagation.
Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated a…
Slow feature analysis (SFA) is a method for extracting slowly varying driving forces from quickly varying nonstationary time series. We show here that it is possible for SFA to detect a component which is even slower than the driving force itself (e.g. the envelope of a modulated sine wave). It is shown that it depends…
SRMD uses random features for efficient time-frequency analysis.
New FGSPCA method captures grouping and sparse structures in PCA without prior info.
DFSOS improves sparse discriminant analysis for high-dimensional data.
Canonical Correlation Analysis (CCA) is a widely used statistical tool with both well established theory and favorable performance for a wide range of machine learning problems. However, computing CCA for huge datasets can be very slow since it involves implementing QR decomposition or singular value decomposition of h…
We present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when one is interested in (or limited to) a primal representation for the first view while having a dual representation for the second view…
Methodology for learning sparse models using all multiplicative interactions efficiently.
Topological data analysis (TDA) has emerged as one of the most promising techniques to reconstruct the unknown shapes of high-dimensional spaces from observed data samples. TDA, thus, yields key shape descriptors in the form of persistent topological features that can be used for any supervised or unsupervised learning…
New method prevents posterior collapse in generative models.
A new distributed algorithm for fitting sparse additive models with feature division and decorrelation.
Proposes a flexible feature allocation model for sparse factor analysis.
Many modern big data applications feature large scale in both numbers of responses and predictors. Better statistical efficiency and scientific insights can be enabled by understanding the large-scale response-predictor association network structures via layers of sparse latent factors ranked by importance. Yet sparsit…
Due to advances in sensors, growing large and complex medical image data have the ability to visualize the pathological change in the cellular or even the molecular level or anatomical changes in tissues and organs. As a consequence, the medical images have the potential to enhance diagnosis of disease, prediction of c…
A new framework for sparse regression models with slow variations.
DFR reduces the computational cost of sparse-group lasso and adaptive sparse-group lasso.
We consider solving the -regularized least-squares (-LS) problem in the context of sparse recovery, for applications such as compressed sensing. The standard proximal gradient method, also known as iterative soft-thresholding when applied to this problem, has low computational cost per iteration but a r…
Proposes PSCCA for estimating correlations and canonical correlations in sparse count data.
A new method scales sparse machine learning to ultra-high dimensional problems.
Optimal sparse recovery with decision stumps achieves strong feature selection guarantees.
Adaptive regularization prevents overfitting in large-scale sparse feature models.
New algorithm clusters sparse data effectively.
Pruning method removes less important features in linear models.
SNS accelerates Sinkhorn algorithm with sparse Newton iterations.
Optimal feature transfer identified through bias-variance analysis.
Sparse PCA provides a linear combination of small number of features that maximizes variance across data. Although Sparse PCA has apparent advantages compared to PCA, such as better interpretability, it is generally thought to be computationally much more expensive. In this paper, we demonstrate the surprising fact tha…
cuRegOT accelerates GPU-based entropic OT solving.
We consider the scenario where one observes an outcome variable and sets of features from multiple assays, all measured on the same set of samples. One approach that has been proposed for dealing with this type of data is ``sparse multiple canonical correlation analysis'' (sparse mCCA). All of the current sparse mCCA t…
Study on Sturm-Liouville problems with zero potential and Neumann boundary conditions.
A brain computer interface (BCI) is a system which provides direct communication between the mind of a person and the outside world by using only brain activity (EEG). The event-related potential (ERP)-based BCI problem consists of a binary pattern recognition. Linear discriminant analysis (LDA) is widely used to solve…