Low-rank Dictionary Learning for Unsupervised Feature Selection
- URL: http://arxiv.org/abs/2106.11102v1
- Date: Mon, 21 Jun 2021 13:39:10 GMT
- Title: Low-rank Dictionary Learning for Unsupervised Feature Selection
- Authors: Mohsen Ghassemi Parsa, Hadi Zare, Mehdi Ghatee
- Abstract summary: We introduce a novel unsupervised feature selection approach by applying dictionary learning ideas in a low-rank representation.
A unified objective function for unsupervised feature selection is proposed in a sparse way by an $ell_2,1$-norm regularization.
Our experimental findings reveal that the proposed method outperforms the state-of-the-art algorithm.
- Score: 11.634317251468968
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: There exist many high-dimensional data in real-world applications such as
biology, computer vision, and social networks. Feature selection approaches are
devised to confront with high-dimensional data challenges with the aim of
efficient learning technologies as well as reduction of models complexity. Due
to the hardship of labeling on these datasets, there are a variety of
approaches on feature selection process in an unsupervised setting by
considering some important characteristics of data. In this paper, we introduce
a novel unsupervised feature selection approach by applying dictionary learning
ideas in a low-rank representation. Dictionary learning in a low-rank
representation not only enables us to provide a new representation, but it also
maintains feature correlation. Then, spectral analysis is employed to preserve
sample similarities. Finally, a unified objective function for unsupervised
feature selection is proposed in a sparse way by an $\ell_{2,1}$-norm
regularization. Furthermore, an efficient numerical algorithm is designed to
solve the corresponding optimization problem. We demonstrate the performance of
the proposed method based on a variety of standard datasets from different
applied domains. Our experimental findings reveal that the proposed method
outperforms the state-of-the-art algorithm.
Related papers
- A Contrast Based Feature Selection Algorithm for High-dimensional Data
set in Machine Learning [9.596923373834093]
We propose a novel filter feature selection method, ContrastFS, which selects discriminative features based on the discrepancies features shown between different classes.
We validate effectiveness and efficiency of our approach on several widely studied benchmark datasets, results show that the new method performs favorably with negligible computation.
arXiv Detail & Related papers (2024-01-15T05:32:35Z) - Compactness Score: A Fast Filter Method for Unsupervised Feature
Selection [66.84571085643928]
We propose a fast unsupervised feature selection method, named as, Compactness Score (CSUFS) to select desired features.
Our proposed algorithm seems to be more accurate and efficient compared with existing algorithms.
arXiv Detail & Related papers (2022-01-31T13:01:37Z) - Budget-aware Few-shot Learning via Graph Convolutional Network [56.41899553037247]
This paper tackles the problem of few-shot learning, which aims to learn new visual concepts from a few examples.
A common problem setting in few-shot classification assumes random sampling strategy in acquiring data labels.
We introduce a new budget-aware few-shot learning problem that aims to learn novel object categories.
arXiv Detail & Related papers (2022-01-07T02:46:35Z) - Auto-weighted Multi-view Feature Selection with Graph Optimization [90.26124046530319]
We propose a novel unsupervised multi-view feature selection model based on graph learning.
The contributions are threefold: (1) during the feature selection procedure, the consensus similarity graph shared by different views is learned.
Experiments on various datasets demonstrate the superiority of the proposed method compared with the state-of-the-art methods.
arXiv Detail & Related papers (2021-04-11T03:25:25Z) - Meta-learning One-class Classifiers with Eigenvalue Solvers for
Supervised Anomaly Detection [55.888835686183995]
We propose a neural network-based meta-learning method for supervised anomaly detection.
We experimentally demonstrate that the proposed method achieves better performance than existing anomaly detection and few-shot learning methods.
arXiv Detail & Related papers (2021-03-01T01:43:04Z) - Adaptive Graph-based Generalized Regression Model for Unsupervised
Feature Selection [11.214334712819396]
How to select the uncorrelated and discriminative features is the key problem of unsupervised feature selection.
We present a novel generalized regression model imposed by an uncorrelated constraint and the $ell_2,1$-norm regularization.
It can simultaneously select the uncorrelated and discriminative features as well as reduce the variance of these data points belonging to the same neighborhood.
arXiv Detail & Related papers (2020-12-27T09:07:26Z) - Minimax Active Learning [61.729667575374606]
Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator.
Current active learning techniques either rely on model uncertainty to select the most uncertain samples or use clustering or reconstruction to choose the most diverse set of unlabeled examples.
We develop a semi-supervised minimax entropy-based active learning algorithm that leverages both uncertainty and diversity in an adversarial manner.
arXiv Detail & Related papers (2020-12-18T19:03:40Z) - Joint Adaptive Graph and Structured Sparsity Regularization for
Unsupervised Feature Selection [6.41804410246642]
We propose a joint adaptive graph and structured sparsity regularization unsupervised feature selection (JASFS) method.
A subset of optimal features will be selected in group, and the number of selected features will be determined automatically.
Experimental results on eight benchmarks demonstrate the effectiveness and efficiency of the proposed method.
arXiv Detail & Related papers (2020-10-09T08:17:04Z) - Learning What Makes a Difference from Counterfactual Examples and
Gradient Supervision [57.14468881854616]
We propose an auxiliary training objective that improves the generalization capabilities of neural networks.
We use pairs of minimally-different examples with different labels, a.k.a counterfactual or contrasting examples, which provide a signal indicative of the underlying causal structure of the task.
Models trained with this technique demonstrate improved performance on out-of-distribution test sets.
arXiv Detail & Related papers (2020-04-20T02:47:49Z) - IVFS: Simple and Efficient Feature Selection for High Dimensional
Topology Preservation [33.424663018395684]
We propose a simple and effective feature selection algorithm to enhance sample similarity preservation.
The proposed algorithm is able to well preserve the pairwise distances, as well as topological patterns, of the full data.
arXiv Detail & Related papers (2020-04-02T23:05:00Z)
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.