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.
A new method for LDA using randomized Kaczmarz improves accuracy for large datasets.
New method uses random discriminators to train GANs more efficiently.
Proposes KMvDA for object recognition from multi-view data.
Proposes improved classification via transfer learning with regularized linear discriminant analysis.
For mass spectra acquired from cancer patients by MALDI or SELDI techniques, automated discrimination between cancer types or stages has often been implemented by machine learnings. These techniques typically generate "black-box" classifiers, which are difficult to interpret biologically. We develop new and efficient s…
Naive Bayes can be used as a discriminative classifier, matching the definition of logistic regression.
We identify conditional parity as a general notion of non-discrimination in machine learning. In fact, several recently proposed notions of non-discrimination, including a few counterfactual notions, are instances of conditional parity. We show that conditional parity is amenable to statistical analysis by studying ran…
PBN combines generative and discriminative capabilities in a neural network.
A new adaptive kNN classifier outperforms Random Forests.
Fisher discriminant analysis (FDA) is a widely used method for classification and dimensionality reduction. When the number of predictor variables greatly exceeds the number of observations, one of the alternatives for conventional FDA is regularized Fisher discriminant analysis (RFDA). In this paper, we present a simp…
Shapelet is a discriminative subsequence of time series. An advanced shapelet-based method is to embed shapelet into accurate and fast random forest. However, it shows several limitations. First, random shapelet forest requires a large training cost for split threshold searching. Second, a single shapelet provides limi…
Multi-output Gaussian processes (MOGP) are probability distributions over vector-valued functions, and have been previously used for multi-output regression and for multi-class classification. A less explored facet of the multi-output Gaussian process is that it can be used as a generative model for vector-valued rando…
Kernel discriminant analysis uses nonlinear embeddings to improve classification.
Hidden Markov Chains and Linear-chain CRFs are equivalent.
Improved LDA method for better classification and dimensionality reduction.
We consider a neural network architecture with randomized features, a sign-splitter, followed by rectified linear units (ReLU). We prove that our architecture exhibits robustness to the input perturbation: the output feature of the neural network exhibits a Lipschitz continuity in terms of the input perturbation. We fu…
Random Forests are reinterpreted as generative models to handle missing data and detect outliers.
In this paper, we consider voxel selection for functional Magnetic Resonance Imaging (fMRI) brain data with the aim of finding a more complete set of probably correlated discriminative voxels, thus improving interpretation of the discovered potential biomarkers. The main difficulty in doing this is an extremely high di…
New method speeds up solving orthogonality constrained problems.
Researchers use information geometry to analyze and improve DRWs for node classification.
New GAN formulation addresses mode collapse issue.
The paper analyzes an ensemble of randomly projected linear discriminants for high-dimensional data.
New method compresses large sample data for faster discriminant analysis.
The paper improves classification accuracy by leveraging a shared signal across domains in high-dimensional classification.
New method for tensor classification with missing data.
A new nonparametric test measures dependence between variables using decision trees.
Fisher's linear discriminant analysis (FLDA) is an important dimension reduction method in statistical pattern recognition. It has been shown that FLDA is asymptotically Bayes optimal under the homoscedastic Gaussian assumption. However, this classical result has the following two major limitations: 1) it holds only fo…
The pattern theory of Grenander is a mathematical framework where patterns are represented by probability models on random variables of algebraic structures. In this paper, we review three families of probability models, namely, the discriminative models, the descriptive models, and the generative models. A discriminat…
SEDA improves RLDA for high-dimensional data.
Improved SVRG for quadratic functions achieves better performance and running times.
ADEC addresses feature randomness and drift in autoencoder-based clustering.
GraphMoE generates random graphs using neural networks and graphlets.
Paper compares generative and discriminative models in uncertainty quantification.
Relying on recent advances in statistical estimation of covariance distances based on random matrix theory, this article proposes an improved covariance and precision matrix estimation for a wide family of metrics. The method is shown to largely outperform the sample covariance matrix estimate and to compete with state…
G-PATE generates private data with high utility using teacher-discriminator aggregation.
Image understanding is an important research domain in the computer vision due to its wide real-world applications. For an image understanding framework that uses the Bag-of-Words model representation, the visual codebook is an essential part. Random forest (RF) as a tree-structure discriminative codebook has been a po…
We provide a unified analysis of the predictive risk of ridge regression and regularized discriminant analysis in a dense random effects model. We work in a high-dimensional asymptotic regime where and , and allow for arbitrary covariance among the features. For both metho…
In this work, dynamic Bayesian multinets are introduced where a Markov chain state at time t determines conditional independence patterns between random variables lying within a local time window surrounding t. It is shown how information-theoretic criterion functions can be used to induce sparse, discriminative, and c…
Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input to a sample that the discriminator seeks to distinguish. We propose a new GAN called Bayesian Conditional Generative Adversarial Networks (BC-GANs) that use a random generator function…
Random Projection (RP) technique has been widely applied in many scenarios because it can reduce high-dimensional features into low-dimensional space within short time and meet the need of real-time analysis of massive data. There is an urgent need of dimensionality reduction with fast increase of big genomics data. Ho…
A Discriminative Deep Forest (DisDF) as a metric learning algorithm is proposed in the paper. It is based on the Deep Forest or gcForest proposed by Zhou and Feng and can be viewed as a gcForest modification. The case of the fully supervised learning is studied when the class labels of individual training examples are …
Improved unsupervised anomaly detection using Random Forest.
GeFs use deep generative models to enhance prediction robustness and uncertainty.
Novel unsupervised random forests improve density estimation and data synthesis.
This article carries out a large dimensional analysis of standard regularized discriminant analysis classifiers designed on the assumption that data arise from a Gaussian mixture model with different means and covariances. The analysis relies on fundamental results from random matrix theory (RMT) when both the number o…
A new framework separates classifier calibration and discrimination.
New method trains generative models without discriminators, improving stability and accuracy.
Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such as building smart environments, or providing intelligent assistance. Traditional approaches to this problem typically rely on supervised lea…