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.
The visibility transformation embeds data position into signature features for efficient pattern recognition.
We propose an iterative scheme for feature-based positioning using a new weighted dissimilarity measure with the goal of reducing the impact of large errors among the measured or modeled features. The weights are computed from the location-dependent standard deviations of the features and stored as part of the referenc…
Forecasting the future traffic flow distribution in an area is an important issue for traffic management in an intelligent transportation system. The key challenge of traffic prediction is to capture spatial and temporal relations between future traffic flows and historical traffic due to highly dynamical patterns of h…
Fingerprinting-based positioning, one of the promising indoor positioning solutions, has been broadly explored owing to the pervasiveness of sensor-rich mobile devices, the prosperity of opportunistically measurable location-relevant signals and the progress of data-driven algorithms. One critical challenge is to contr…
New method improves feature selection by integrating stability paths.
A new DRL model for intraday trading incorporating positional context.
While invasively recorded brain activity is known to provide detailed information on motor commands, it is an open question at what level of detail information about positions of body parts can be decoded from non-invasively acquired signals. In this work it is shown that index finger positions can be differentiated fr…
The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as "positive" together with a large volume of "unlabeled" samples that may contain both positive and negative samples. Bu…
Proposes fwelnet to improve prediction using feature information.
We propose an approach to reduce both computational complexity and data storage requirements for the online positioning stage of a fingerprinting-based indoor positioning system (FIPS) by introducing segmentation of the region of interest (RoI) into sub-regions, sub-region selection using a modified Jaccard index, and …
Proposes cost-sensitive feature selection for SVMs.
This paper proposed a method for stock prediction. In terms of feature extraction, we extract the features of stock-related news besides stock prices. We first select some seed words based on experience which are the symbols of good news and bad news. Then we propose an optimization method and calculate the positive po…
Study k-positive surface group representations and their degenerations.
The paper tackles feature cross search for linear models, providing approximation algorithms and structural results.
We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative data embeddings distribute compactly to form a positive component and negative component respectively, while the positive component and nega…
Novel unsupervised feature selection method using multi-step Markov transition probability.
Paper proposes a statistical test for feature selection pipelines using selective inference.
This paper presents a spermwhale' localization architecture using jointly a bag-of-features (BoF) approach and machine learning framework. BoF methods are known, especially in computer vision, to produce from a collection of local features a global representation invariant to principal signal transformations. Our idea …
SAEs struggle with feature consistency across runs, hindering MI reliability.
Paper solves open question about non-positive kernels by decomposing them into PD kernels.
Nonparametric IPSS selects features with false discovery control.
The paper proposes a method to test features selected by SeqFS-DA with controlled FPR.
New random forest algorithms for PU learning minimize risk directly.
A new method predicts true classes from positive and unlabeled data with additional labeled observations.
Enhanced financial reward with shuffled feature CNN-DRL.
In this paper, we use the flag curvature formula for homogeneous Finsler spaces in our previous work to classify odd dimensional smooth coset spaces admitting positively curved reversible homogeneous Finsler metrics. We will show that the most features of L. Bérard-Bergery's classification results for odd dimensional p…
The study explains how transformer components enable in-context learning.
Proposes a new scoring function for linear classifiers to improve object positioning in feature space.
Extends conformal prediction to contrastive learning for better coverage of positive samples.
Study proves existence, uniqueness, and positivity of solutions to a complex volatility model.
SFS-DA method statistically tests FS reliability under domain adaptation.
Positive-definite kernel functions are fundamental elements of kernel methods and Gaussian processes. A well-known construction of such functions comes from Bochner's characterization, which connects a positive-definite function with a probability distribution. Another construction, which appears to have attracted less…
Feature selection has attracted significant attention in data mining and machine learning in the past decades. Many existing feature selection methods eliminate redundancy by measuring pairwise inter-correlation of features, whereas the complementariness of features and higher inter-correlation among more than two feat…
A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels. Given inaccurate training labels, DL-FUMI learns a set of target dictionary atoms that describe the most distincti…
Graph neural network using Beltrami flow for feature and topology evolution.
Paper introduces PTL-SI for statistical inference in TL-HDR, controlling FPR.
New RFs reduce kernel approximation variance and improve Transformer performance.
In cheminformatics, compound-target binding profiles has been a main source of data for research. For data repositories that only provide positive profiles, a popular assumption is that unreported profiles are all negative. In this paper, we caution audience not to take this assumption for granted, and present empirica…
This paper investigates two feature-scoring criteria that make use of estimated class probabilities: one method proposed by \citet{shen} and a complementary approach proposed below. We develop a theoretical framework to analyze each criterion and show that both estimate the spread (across all values of a given feature)…
Machine-learned models are often described as "black boxes". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretability. When this is the case, feature engineering becomes a crucial task, which requires significant and time-consuming human effort. Whilst s…
New approach selects sparse features without validation.
In many real-world scenarios where data is high dimensional, test time acquisition of features is a non-trivial task due to costs associated with feature acquisition and evaluating feature value. The need for highly confident models with an extremely frugal acquisition of features can be addressed by allowing a feature…
New spectral mixture representation for isotropic kernels simplifies random Fourier features.
DHGAK aligns substructures for better graph kernel performance.
Improved disentanglement through learned feature aggregation.
This work optimizes alignment and uniformity of features on a hypersphere for better downstream performance.
Paper proposes sparse classification method for high-dimensional data.
New algorithm for truncated linear regression without knowing the survival set.