Infinite Feature Selection: A Graph-based Feature Filtering Approach

• URL: http://arxiv.org/abs/2006.08184v1
• Date: Mon, 15 Jun 2020 07:20:40 GMT
• Title: Infinite Feature Selection: A Graph-based Feature Filtering Approach
• Authors: Giorgio Roffo, Simone Melzi, Umberto Castellani, Alessandro Vinciarelli, Marco Cristani
• Abstract summary: We propose a filtering feature selection framework that considers subsets of features as paths in a graph. Going to infinite allows to constrain the computational complexity of the selection process. We show that Inf-FS behaves better in almost any situation, that is, when the number of features to keep are fixed a priori.
• Score: 78.63188057505012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a filtering feature selection framework that considers subsets of features as paths in a graph, where a node is a feature and an edge indicates pairwise (customizable) relations among features, dealing with relevance and redundancy principles. By two different interpretations (exploiting properties of power series of matrices and relying on Markov chains fundamentals) we can evaluate the values of paths (i.e., feature subsets) of arbitrary lengths, eventually go to infinite, from which we dub our framework Infinite Feature Selection (Inf-FS). Going to infinite allows to constrain the computational complexity of the selection process, and to rank the features in an elegant way, that is, considering the value of any path (subset) containing a particular feature. We also propose a simple unsupervised strategy to cut the ranking, so providing the subset of features to keep. In the experiments, we analyze diverse settings with heterogeneous features, for a total of 11 benchmarks, comparing against 18 widely-known comparative approaches. The results show that Inf-FS behaves better in almost any situation, that is, when the number of features to keep are fixed a priori, or when the decision of the subset cardinality is part of the process.

Related papers

• A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling [54.05517338122698]
  We propose an explicitly controllable query-key feature alignment from both semantic-aware and detail-aware perspectives. We also develop a fine-grained neighbor selection strategy on HR features, which is simple yet effective for alleviating mosaic artifacts. Our proposed ReSFU framework consistently achieves satisfactory performance on different segmentation applications.
  arXiv  Detail & Related papers  (2024-07-02T14:12:21Z)
• Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent [44.84307718534031]
  We introduce an innovative framework for feature selection guided by knockoff features and optimized through reinforcement learning. A deep Q-network, pre-trained with the original features and their corresponding pseudo labels, is employed to improve the efficacy of the exploration process. A new epsilon-greedy strategy is used, incorporating insights from the pseudo labels to make the feature selection process more effective.
  arXiv  Detail & Related papers  (2024-03-06T19:58:19Z)
• Feature Selection as Deep Sequential Generative Learning [50.00973409680637]
  We develop a deep variational transformer model over a joint of sequential reconstruction, variational, and performance evaluator losses. Our model can distill feature selection knowledge and learn a continuous embedding space to map feature selection decision sequences into embedding vectors associated with utility scores.
  arXiv  Detail & Related papers  (2024-03-06T16:31:56Z)
• Parallel feature selection based on the trace ratio criterion [4.30274561163157]
  This work presents a novel parallel feature selection approach for classification, namely Parallel Feature Selection using Trace criterion (PFST) Our method uses trace criterion, a measure of class separability used in Fisher's Discriminant Analysis, to evaluate feature usefulness. The experiments show that our method can produce a small set of features in a fraction of the amount of time by the other methods under comparison.
  arXiv  Detail & Related papers  (2022-03-03T10:50:33Z)
• CFNet: Learning Correlation Functions for One-Stage Panoptic Segmentation [46.252118473248316]
  We propose to first predict semantic-level and instance-level correlations among different locations that are utilized to enhance the backbone features. We then feed the improved discriminative features into the corresponding segmentation heads, respectively. We achieve state-of-the-art performance on MS with $45.1$% PQ and ADE20k with $32.6$% PQ.
  arXiv  Detail & Related papers  (2022-01-13T05:31:14Z)
• Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation [71.27989298860481]
  We address the non- optimisation problem of finding a matrix on the Stiefel manifold that maximises a quadratic objective function. We propose a simple yet effective sparsity-promoting algorithm for finding the dominant eigenspace matrix.
  arXiv  Detail & Related papers  (2021-09-30T19:17:35Z)
• PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation [96.76882806139251]
  We propose a point-wise affinity propagation module based on the Feature Pyramid Network (FPN) framework, named PointFlow. Rather than dense affinity learning, a sparse affinity map is generated upon selected points between the adjacent features. Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods.
  arXiv  Detail & Related papers  (2021-03-11T09:42:32Z)
• Unsupervised Feature Selection via Multi-step Markov Transition Probability [29.717104109481536]
  We propose a novel and very simple approach for unsupervised feature selection, named MMFS. The idea is using multi-step Markov transition probability to describe the relation between any data pair. The performance of our approach is compared with the state-of-the-art methods on eight real-world data sets.
  arXiv  Detail & Related papers  (2020-05-29T01:15:16Z)

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.