FINDER: Feature Inference on Noisy Datasets using Eigenspace Residuals

• URL: http://arxiv.org/abs/2510.19917v1
• Date: Wed, 22 Oct 2025 18:00:03 GMT
• Title: FINDER: Feature Inference on Noisy Datasets using Eigenspace Residuals
• Authors: Trajan Murphy, Akshunna S. Dogra, Hanfeng Gu, Caleb Meredith, Mark Kon, Julio Enrique Castrillion-Candas,
• Abstract summary: We introduce FINDER, a rigorous framework for analyzing generic classification problems.<n>FINDER incorporates fundamental analysis ideas into the feature learning and inference stages.<n>We produce state of the art breakthroughs in: (i) Alzheimer's Disease stage classification, (ii) Remote sensing detection of deforestation.
• Score: 0.7349919073512893
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: ''Noisy'' datasets (regimes with low signal to noise ratios, small sample sizes, faulty data collection, etc) remain a key research frontier for classification methods with both theoretical and practical implications. We introduce FINDER, a rigorous framework for analyzing generic classification problems, with tailored algorithms for noisy datasets. FINDER incorporates fundamental stochastic analysis ideas into the feature learning and inference stages to optimally account for the randomness inherent to all empirical datasets. We construct ''stochastic features'' by first viewing empirical datasets as realizations from an underlying random field (without assumptions on its exact distribution) and then mapping them to appropriate Hilbert spaces. The Kosambi-Karhunen-Lo\'eve expansion (KLE) breaks these stochastic features into computable irreducible components, which allow classification over noisy datasets via an eigen-decomposition: data from different classes resides in distinct regions, identified by analyzing the spectrum of the associated operators. We validate FINDER on several challenging, data-deficient scientific domains, producing state of the art breakthroughs in: (i) Alzheimer's Disease stage classification, (ii) Remote sensing detection of deforestation. We end with a discussion on when FINDER is expected to outperform existing methods, its failure modes, and other limitations.

Related papers

• Data-Driven Information-Theoretic Causal Bounds under Unmeasured Confounding [10.590231532335691]
  We develop a data-driven information-theoretic framework for partial identification of conditional causal effects under unmeasured confounding.<n>Our key theoretical contribution shows that the f-divergence between the observational distribution P(Y | A = a, X = x) and the interventional distribution P(Y | do(A = a), X = x) is upper bounded by a function of the propensity score alone.<n>This result enables sharp partial identification of conditional causal effects directly from observational data, without requiring external sensitivity parameters, auxiliary variables, full structural specifications, or outcome boundedness assumptions.
  arXiv  Detail & Related papers  (2026-01-23T20:47:48Z)
• A Dataset for Semantic Segmentation in the Presence of Unknowns [49.795683850385956]
  Existing datasets allow evaluation of only knowns or unknowns - but not both.<n>We propose a novel anomaly segmentation dataset, ISSU, that features a diverse set of anomaly inputs from cluttered real-world environments.<n>The dataset is twice larger than existing anomaly segmentation datasets.
  arXiv  Detail & Related papers  (2025-03-28T10:31:01Z)
• Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation [11.077512630548153]
  Multi-class semantic segmentation remains a cornerstone challenge in computer vision.<n>Active Learning (AL) mitigates this challenge by selecting data points for annotation strategically.<n>We present OREAL, a novel patch-based AL method designed for multi-class semantic segmentation.
  arXiv  Detail & Related papers  (2024-12-09T13:15:52Z)
• Minimally Supervised Learning using Topological Projections in Self-Organizing Maps [55.31182147885694]
  We introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs) Our proposed method first trains SOMs on unlabeled data and then a minimal number of available labeled data points are assigned to key best matching units (BMU) Our results indicate that the proposed minimally supervised model significantly outperforms traditional regression techniques.
  arXiv  Detail & Related papers  (2024-01-12T22:51:48Z)
• Learning to Bound Counterfactual Inference in Structural Causal Models from Observational and Randomised Data [64.96984404868411]
  We derive a likelihood characterisation for the overall data that leads us to extend a previous EM-based algorithm. The new algorithm learns to approximate the (unidentifiability) region of model parameters from such mixed data sources. It delivers interval approximations to counterfactual results, which collapse to points in the identifiable case.
  arXiv  Detail & Related papers  (2022-12-06T12:42:11Z)
• Detection and Evaluation of Clusters within Sequential Data [58.720142291102135]
  Clustering algorithms for Block Markov Chains possess theoretical optimality guarantees. In particular, our sequential data is derived from human DNA, written text, animal movement data and financial markets. It is found that the Block Markov Chain model assumption can indeed produce meaningful insights in exploratory data analyses.
  arXiv  Detail & Related papers  (2022-10-04T15:22:39Z)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Efficient Multidimensional Functional Data Analysis Using Marginal Product Basis Systems [2.4554686192257424]
  We propose a framework for learning continuous representations from a sample of multidimensional functional data. We show that the resulting estimation problem can be solved efficiently by the tensor decomposition. We conclude with a real data application in neuroimaging.
  arXiv  Detail & Related papers  (2021-07-30T16:02:15Z)
• Online nonnegative CP-dictionary learning for Markovian data [8.490619842547739]
  We introduce a novel algorithm that learns a CANDECOMP/PARAFAC basis from a given stream of tensor-valued data under general constraints. We prove that our algorithm converges almost surely to the set of stationary points of the objective function under the hypothesis that the sequence of data tensors is generated by an underlying Markov chain.
  arXiv  Detail & Related papers  (2020-09-16T11:41:01Z)
• Spatial Classification With Limited Observations Based On Physics-Aware Structural Constraint [18.070762916388272]
  spatial classification with limited feature observations has been a challenging problem in machine learning. This paper extends our recent approach by allowing feature values of samples in each class to follow a multi-modal distribution. We propose learning algorithms for the extended model with multi-modal distribution.
  arXiv  Detail & Related papers  (2020-08-25T20:07:28Z)
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data [66.78671826743884]
  The distributionally robust optimization framework is considered for training a parametric model. The objective is to endow the trained model with robustness against adversarially manipulated input data. Proposed algorithms offer robustness with little overhead.
  arXiv  Detail & Related papers  (2020-07-07T18:25:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.