Logifold: A Geometrical Foundation of Ensemble Machine Learning

• URL: http://arxiv.org/abs/2407.16177v2
• Date: Sat, 19 Oct 2024 02:36:57 GMT
• Title: Logifold: A Geometrical Foundation of Ensemble Machine Learning
• Authors: Inkee Jung, Siu-Cheong Lau,
• Abstract summary: We present a local-to-global and measure-theoretical approach to understanding datasets. The core idea is to formulate a logifold structure and to interpret network models with restricted domains as local charts of datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a local-to-global and measure-theoretical approach to understanding datasets. The core idea is to formulate a logifold structure and to interpret network models with restricted domains as local charts of datasets. In particular, this provides a mathematical foundation for ensemble machine learning. Our experiments demonstrate that logifolds can be implemented to identify fuzzy domains and improve accuracy compared to taking average of model outputs. Additionally, we provide a theoretical example of a logifold, highlighting the importance of restricting to domains of classifiers in an ensemble.

Related papers

• A logifold structure on measure space [0.0]
  We develop a local-to-global and measure-theoretical approach to understand datasets. We show in experiments how it can be used to find fuzzy domains and to improve accuracy in data classification problems.
  arXiv  Detail & Related papers  (2024-05-09T01:38:38Z)
• Prospector Heads: Generalized Feature Attribution for Large Models & Data [82.02696069543454]
  We introduce prospector heads, an efficient and interpretable alternative to explanation-based attribution methods. We demonstrate how prospector heads enable improved interpretation and discovery of class-specific patterns in input data.
  arXiv  Detail & Related papers  (2024-02-18T23:01:28Z)
• Tackling Computational Heterogeneity in FL: A Few Theoretical Insights [68.8204255655161]
  We introduce and analyse a novel aggregation framework that allows for formalizing and tackling computational heterogeneous data. Proposed aggregation algorithms are extensively analyzed from a theoretical, and an experimental prospective.
  arXiv  Detail & Related papers  (2023-07-12T16:28:21Z)
• Gated Domain Units for Multi-source Domain Generalization [14.643490853965385]
  Distribution shift (DS) occurs when a dataset at test time differs from the dataset at training time. We introduce a modular neural network layer consisting of Gated Domain Units (GDUs) that learn a representation for each latent elementary distribution. During inference, a weighted ensemble of learning machines can be created by comparing new observations with the representations of each elementary distribution.
  arXiv  Detail & Related papers  (2022-06-24T18:12:38Z)
• Inducing Gaussian Process Networks [80.40892394020797]
  We propose inducing Gaussian process networks (IGN), a simple framework for simultaneously learning the feature space as well as the inducing points. The inducing points, in particular, are learned directly in the feature space, enabling a seamless representation of complex structured domains. We report on experimental results for real-world data sets showing that IGNs provide significant advances over state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-21T05:27:09Z)
• Incorporation of Deep Neural Network & Reinforcement Learning with Domain Knowledge [0.0]
  We present a study of the manners by which Domain information has been incorporated when building models with Neural Networks. Integrating space data is uniquely important to the development of Knowledge understanding model, as well as other fields that aid in understanding information by utilizing the human-machine interface and Reinforcement Learning.
  arXiv  Detail & Related papers  (2021-07-29T17:29:02Z)
• A Topological-Framework to Improve Analysis of Machine Learning Model Performance [5.3893373617126565]
  We propose a framework for evaluating machine learning models in which a dataset is treated as a "space" on which a model operates. We describe a topological data structure, presheaves, which offer a convenient way to store and analyze model performance between different subpopulations.
  arXiv  Detail & Related papers  (2021-07-09T23:11:13Z)
• Self-supervised Graph-level Representation Learning with Local and Global Structure [71.45196938842608]
  We propose a unified framework called Local-instance and Global-semantic Learning (GraphLoG) for self-supervised whole-graph representation learning. Besides preserving the local similarities, GraphLoG introduces the hierarchical prototypes to capture the global semantic clusters. An efficient online expectation-maximization (EM) algorithm is further developed for learning the model.
  arXiv  Detail & Related papers  (2021-06-08T05:25:38Z)
• Clustered Federated Learning via Generalized Total Variation Minimization [83.26141667853057]
  We study optimization methods to train local (or personalized) models for local datasets with a decentralized network structure. Our main conceptual contribution is to formulate federated learning as total variation minimization (GTV) Our main algorithmic contribution is a fully decentralized federated learning algorithm.
  arXiv  Detail & Related papers  (2021-05-26T18:07:19Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)

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.