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.
SQFA learns features maximizing Fisher-Rao distance for better classification.
Adapts DR objectives for both sample and feature size reduction.
t-SNE loses important features in data visualization.
PSMM method optimizes matrix sufficient dimension reduction.
A neural network approach for feature selection using mutual information.
Develops a new method for nonlinear dimension reduction using random features.
Novel bounds for logistic regression coreset construction and feature selection.
Affective computing has become a very important research area in human-machine interaction. However, affects are subjective, subtle, and uncertain. So, it is very difficult to obtain a large number of labeled training samples, compared with the number of possible features we could extract. Thus, dimensionality reductio…
Study reduces dimensions for -means clustering for better accuracy.
Two new algorithms reduce feature space while preserving non-linear relationships.
UniFeat is an open-source Java tool for feature selection.
A key question in Reinforcement Learning is which representation an agent can learn to efficiently reuse knowledge between different tasks. Recently the Successor Representation was shown to have empirical benefits for transferring knowledge between tasks with shared transition dynamics. This paper presents Model Featu…
LDA improves image classification accuracy with fewer features.
CCP clusters correlated features and projects them to 1D for efficient dimensionality reduction.
Study evaluates feature ranking methods' faithfulness in ML models, improving with dimensionality reduction.
One of the major advantages in using Deep Learning for Finance is to embed a large collection of information into investment decisions. A way to do that is by means of compression, that lead us to consider a smaller feature space. Several studies are proving that non-linear feature reduction performed by Deep Learning …
Irregular features disrupt the desired classification. In this paper, we consider aggressively modifying scales of features in the original space according to the label information to form well-separated clusters in low-dimensional space. The proposed method exploits spectral clustering to derive scaling factors that a…
Spectral dimensionality reduction methods enable linear separations of complex data with high-dimensional features in a reduced space. However, these methods do not always give the desired results due to irregularities or uncertainties of the data. Thus, we consider aggressively modifying the scales of the features to …
DFR reduces the computational cost of sparse-group lasso and adaptive sparse-group lasso.
Supervised linear feature extraction can be achieved by fitting a reduced rank multivariate model. This paper studies rank penalized and rank constrained vector generalized linear models. From the perspective of thresholding rules, we build a framework for fitting singular value penalized models and use it for feature …
Unified framework for graph coarsening using node features and graph matrices.
In this work, we revisit fast dimension reduction approaches, as with random projections and random sampling. Our goal is to summarize the data to decrease computational costs and memory footprint of subsequent analysis. Such dimension reduction can be very efficient when the signals of interest have a strong structure…
BasisVAE combines VAE and clustering for tabular data analysis.
New method uses sparse random features for crashworthiness analysis.
Paper proposes a novel unsupervised feature selection method using K-means and ADMM.
In our work, we propose a novel formulation for supervised dimensionality reduction based on a nonlinear dependency criterion called Statistical Distance Correlation, Szekely et. al. (2007). We propose an objective which is free of distributional assumptions on regression variables and regression model assumptions. Our…
This paper introduces a new unsupervised method for dimensionality reduction via regression (DRR). The algorithm belongs to the family of invertible transforms that generalize Principal Component Analysis (PCA) by using curvilinear instead of linear features. DRR identifies the nonlinear features through multivariate r…
TrIM improves gradient-based dimension reduction and regression.
Random features are improved by variance-reducing couplings, enhancing machine learning models.
In online advertising, display ads are increasingly being placed based on real-time auctions where the advertiser who wins gets to serve the ad. This is called real-time bidding (RTB). In RTB, auctions have very tight time constraints on the order of 100ms. Therefore mechanisms for bidding intelligently such as clickth…
In this paper we propose a novel variance reduction approach for additive functionals of Markov chains based on minimization of an estimate for the asymptotic variance of these functionals over suitable classes of control variates. A distinctive feature of the proposed approach is its ability to significantly reduce th…
Study reduces redundant information in multi-modal datasets.
Many machine learning problems, especially multi-modal learning problems, have two sets of distinct features (e.g., image and text features in news story classification, or neuroimaging data and neurocognitive data in cognitive science research). This paper addresses the joint dimensionality reduction of two feature ve…
Paper proposes a tensor data model for incomplete imaging data.
Value selection reduces model size while maintaining accuracy.
RFM reduces feature space for linear models, improving sparse recovery.
A new tensor-based layer reduces neural network dimensions without losing important features.
Linear dimensionality reduction techniques are powerful tools for image analysis as they allow the identification of important features in a data set. In particular, nonnegative matrix factorization (NMF) has become very popular as it is able to extract sparse, localized and easily interpretable features by imposing an…
A method constructs a stochastic surrogate from dimensionality reduction results for high-dimensional uncertainty quantification.
FEALM learns features for better nonlinear DR of hidden patterns.
Paper proposes an unsupervised feature selection algorithm with stability guarantees.
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through s…
This work improves understanding of dimension reduction algorithms and their probabilistic embeddings.
Proposes a flexible feature allocation model for sparse factor analysis.
Many features of dimensional reduction schemes are determined by the breaking of higher dimensional general covariance associated with the selection of a particular subset of coordinates. By investigating residual covariance we introduce lower dimensional tensors --generalizing to one side Kaluza-Klein gauge fields and…
We focus in this paper on dataset reduction techniques for use in k-nearest neighbor classification. In such a context, feature and prototype selections have always been independently treated by the standard storage reduction algorithms. While this certifying is theoretically justified by the fact that each subproblem …
An important step in speaker verification is extracting features that best characterize the speaker voice. This paper investigates a front-end processing that aims at improving the performance of speaker verification based on the SVMs classifier, in text independent mode. This approach combines features based on conven…
Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible for data with millions of features. Here we introduce the first feature selection method for nonlinear learning problems that can scale up t…