Linear-time One-Class Classification with Repeated Element-wise Folding

• URL: http://arxiv.org/abs/2408.11412v1
• Date: Wed, 21 Aug 2024 08:18:39 GMT
• Title: Linear-time One-Class Classification with Repeated Element-wise Folding
• Authors: Jenni Raitoharju,
• Abstract summary: This paper proposes an easy-to-use method for one-class classification: Repeated Element-wise Folding (REF) REF provides a linear-time alternative for the commonly used computationally much more demanding approaches. Experiments show that REF can produce similar classification performance or even outperform the more complex algorithms on various benchmark datasets.
• Score: 6.116088814650622
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper proposes an easy-to-use method for one-class classification: Repeated Element-wise Folding (REF). The algorithm consists of repeatedly standardizing and applying an element-wise folding operation on the one-class training data. Equivalent mappings are performed on unknown test items and the classification prediction is based on the item's distance to the origin of the final distribution. As all the included operations have linear time complexity, the proposed algorithm provides a linear-time alternative for the commonly used computationally much more demanding approaches. Furthermore, REF can avoid the challenges of hyperparameter setting in one-class classification by providing robust default settings. The experiments show that the proposed method can produce similar classification performance or even outperform the more complex algorithms on various benchmark datasets. Matlab codes for REF are publicly available at https://github.com/JenniRaitoharju/REF.

Related papers

• A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation [121.0693322732454]
  Contrastive Language-Image Pretraining (CLIP) has gained popularity for its remarkable zero-shot capacity. Recent research has focused on developing efficient fine-tuning methods to enhance CLIP's performance in downstream tasks. We revisit a classical algorithm, Gaussian Discriminant Analysis (GDA), and apply it to the downstream classification of CLIP.
  arXiv  Detail & Related papers  (2024-02-06T15:45:27Z)
• Exploring Category-correlated Feature for Few-shot Image Classification [27.13708881431794]
  We present a simple yet effective feature rectification method by exploring the category correlation between novel and base classes as the prior knowledge. The proposed approach consistently obtains considerable performance gains on three widely used benchmarks.
  arXiv  Detail & Related papers  (2021-12-14T08:25:24Z)
• When in Doubt: Improving Classification Performance with Alternating Normalization [57.39356691967766]
  We introduce Classification with Alternating Normalization (CAN), a non-parametric post-processing step for classification. CAN improves classification accuracy for challenging examples by re-adjusting their predicted class probability distribution. We empirically demonstrate its effectiveness across a diverse set of classification tasks.
  arXiv  Detail & Related papers  (2021-09-28T02:55:42Z)
• Probability-driven scoring functions in combining linear classifiers [0.913755431537592]
  This research is aimed at building a new fusion method dedicated to the ensemble of linear classifiers. The proposed fusion method is compared with the reference method using multiple benchmark datasets taken from the KEEL repository. The experimental study shows that, under certain conditions, some improvement may be obtained.
  arXiv  Detail & Related papers  (2021-09-16T08:58:32Z)
• A concise method for feature selection via normalized frequencies [0.0]
  In this paper, a concise method is proposed for universal feature selection. The proposed method uses a fusion of the filter method and the wrapper method, rather than a combination of them. The evaluation results show that the proposed method outperformed several state-of-the-art related works in terms of accuracy, precision, recall, F-score and AUC.
  arXiv  Detail & Related papers  (2021-06-10T15:29:54Z)
• Class-Incremental Learning with Generative Classifiers [6.570917734205559]
  We propose a new strategy for class-incremental learning: generative classification. Our proposal is to learn the joint distribution p(x,y), factorized as p(x|y)p(y), and to perform classification using Bayes' rule. As a proof-of-principle, here we implement this strategy by training a variational autoencoder for each class to be learned.
  arXiv  Detail & Related papers  (2021-04-20T16:26:14Z)
• SetConv: A New Approach for Learning from Imbalanced Data [29.366843553056594]
  We propose a set convolution operation and an episodic training strategy to extract a single representative for each class. We prove that our proposed algorithm is permutation-invariant despite the order of inputs.
  arXiv  Detail & Related papers  (2021-04-03T22:33:30Z)
• Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification [94.55805516167369]
  We propose a new approach for binary classification from m U-sets for $mge2$. Our key idea is to consider an auxiliary classification task called surrogate set classification (SSC)
  arXiv  Detail & Related papers  (2021-02-01T07:36:38Z)
• Accelerated Message Passing for Entropy-Regularized MAP Inference [89.15658822319928]
  Maximum a posteriori (MAP) inference in discrete-valued random fields is a fundamental problem in machine learning. Due to the difficulty of this problem, linear programming (LP) relaxations are commonly used to derive specialized message passing algorithms. We present randomized methods for accelerating these algorithms by leveraging techniques that underlie classical accelerated gradient.
  arXiv  Detail & Related papers  (2020-07-01T18:43:32Z)
• Certified Robustness to Label-Flipping Attacks via Randomized Smoothing [105.91827623768724]
  Machine learning algorithms are susceptible to data poisoning attacks. We present a unifying view of randomized smoothing over arbitrary functions. We propose a new strategy for building classifiers that are pointwise-certifiably robust to general data poisoning attacks.
  arXiv  Detail & Related papers  (2020-02-07T21:28:30Z)
• Optimal Clustering from Noisy Binary Feedback [75.17453757892152]
  We study the problem of clustering a set of items from binary user feedback. We devise an algorithm with a minimal cluster recovery error rate. For adaptive selection, we develop an algorithm inspired by the derivation of the information-theoretical error lower bounds.
  arXiv  Detail & Related papers  (2019-10-14T09:18:26Z)

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.