Chaotic Map based Compression Approach to Classification

• URL: http://arxiv.org/abs/2502.12302v1
• Date: Mon, 17 Feb 2025 20:22:49 GMT
• Title: Chaotic Map based Compression Approach to Classification
• Authors: Harikrishnan N B, Anuja Vats, Nithin Nagaraj, Marius Pedersen,
• Abstract summary: Modern machine learning approaches prioritize performance at the cost of increased complexity, computational demands, and reduced interpretability. This paper introduces a novel framework that challenges this trend by reinterpreting learning from an information-theoretic perspective. Rather than following the conventional approach of fitting data to complex models, we propose a fundamentally different method that maps data to intervals of initial conditions in a dynamical system.
• Score: 3.3573756702816495
• License:
• Abstract: Modern machine learning approaches often prioritize performance at the cost of increased complexity, computational demands, and reduced interpretability. This paper introduces a novel framework that challenges this trend by reinterpreting learning from an information-theoretic perspective, viewing it as a search for encoding schemes that capture intrinsic data structures through compact representations. Rather than following the conventional approach of fitting data to complex models, we propose a fundamentally different method that maps data to intervals of initial conditions in a dynamical system. Our GLS (Generalized L\"uroth Series) coding compression classifier employs skew tent maps - a class of chaotic maps - both for encoding data into initial conditions and for subsequent recovery. The effectiveness of this simple framework is noteworthy, with performance closely approaching that of well-established machine learning methods. On the breast cancer dataset, our approach achieves 92.98\% accuracy, comparable to Naive Bayes at 94.74\%. While these results do not exceed state-of-the-art performance, the significance of our contribution lies not in outperforming existing methods but in demonstrating that a fundamentally simpler, more interpretable approach can achieve competitive results.

Related papers

• Efficient Fairness-Performance Pareto Front Computation [51.558848491038916]
  We show that optimal fair representations possess several useful structural properties. We then show that these approxing problems can be solved efficiently via concave programming methods.
  arXiv  Detail & Related papers  (2024-09-26T08:46:48Z)
• DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization [44.291382840373]
  This paper addresses the challenge of out-of-distribution generalization in graph machine learning. Traditional graph learning algorithms falter in real-world scenarios where this assumption fails. A principal factor contributing to this suboptimal performance is the inherent simplicity bias of neural networks.
  arXiv  Detail & Related papers  (2024-08-08T12:08:55Z)
• Machine Learning for Predicting Chaotic Systems [0.0]
  Predicting chaotic dynamical systems is critical in many scientific fields, such as weather forecasting. In this paper, we compare different lightweight and heavyweight machine learning architectures. We introduce the cumulative maximum error, a novel metric that combines desirable properties of traditional metrics and is tailored for chaotic systems.
  arXiv  Detail & Related papers  (2024-07-29T16:34:47Z)
• Bayesian Exploration of Pre-trained Models for Low-shot Image Classification [14.211305168954594]
  This work proposes a simple and effective probabilistic model ensemble framework based on Gaussian processes. We achieve the integration of prior knowledge by specifying the mean function with CLIP and the kernel function. We demonstrate that our method consistently outperforms competitive ensemble baselines regarding predictive performance.
  arXiv  Detail & Related papers  (2024-03-30T10:25:28Z)
• Enhancing Surface Neural Implicits with Curvature-Guided Sampling and Uncertainty-Augmented Representations [37.42624848693373]
  We introduce a method that directly digests depth images for the task of high-fidelity 3D reconstruction. A simple sampling strategy is proposed to generate highly effective training data. Despite its simplicity, our method outperforms a range of both classical and learning-based baselines.
  arXiv  Detail & Related papers  (2023-06-03T12:23:17Z)
• Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting [62.23057729112182]
  Differentiable score-based causal discovery methods learn a directed acyclic graph from observational data. We propose a model-agnostic framework to boost causal discovery performance by dynamically learning the adaptive weights for the Reweighted Score function, ReScore.
  arXiv  Detail & Related papers  (2023-03-06T14:49:59Z)
• Supervised Dimensionality Reduction and Classification with Convolutional Autoencoders [1.1164202369517053]
  A Convolutional Autoencoder is combined to simultaneously produce supervised dimensionality reduction and predictions. The resulting Latent Space can be utilized to improve traditional, interpretable classification algorithms. The proposed methodology introduces advanced explainability regarding, not only the data structure through the produced latent space, but also about the classification behaviour.
  arXiv  Detail & Related papers  (2022-08-25T15:18:33Z)
• Deep Equilibrium Assisted Block Sparse Coding of Inter-dependent Signals: Application to Hyperspectral Imaging [71.57324258813675]
  A dataset of inter-dependent signals is defined as a matrix whose columns demonstrate strong dependencies. A neural network is employed to act as structure prior and reveal the underlying signal interdependencies. Deep unrolling and Deep equilibrium based algorithms are developed, forming highly interpretable and concise deep-learning-based architectures.
  arXiv  Detail & Related papers  (2022-03-29T21:00:39Z)
• Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition [97.14064057840089]
  We propose a method to automatically find a compact and problem-specific network for graph convolutional networks in a progressive manner. Experimental results on two datasets for skeleton-based human action recognition indicate that the proposed method has competitive or even better classification performance.
  arXiv  Detail & Related papers  (2020-11-11T09:57:49Z)
• CIMON: Towards High-quality Hash Codes [63.37321228830102]
  We propose a new method named textbfComprehensive stextbfImilarity textbfMining and ctextbfOnsistency leartextbfNing (CIMON) First, we use global refinement and similarity statistical distribution to obtain reliable and smooth guidance. Second, both semantic and contrastive consistency learning are introduced to derive both disturb-invariant and discriminative hash codes.
  arXiv  Detail & Related papers  (2020-10-15T14:47:14Z)
• Semi-Supervised Learning with Meta-Gradient [123.26748223837802]
  We propose a simple yet effective meta-learning algorithm in semi-supervised learning. We find that the proposed algorithm performs favorably against state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-08T08:48:56Z)

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.