Structure Preserving Non-negative Feature Self-Representation for Unsupervised Feature Selection

Zhou, Wei; Wu, Chengdong; Yi, Yugen; Luo, Guoliang

doi:10.1109/access.2017.2699741

Cited by 32 publications

(21 citation statements)

References 21 publications

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“…Each sample is given a weight that reflects its representational ability. The most representative samples are selected as dictionary atoms [23–28]. …”

Section: The Proposed Methodsmentioning

confidence: 99%

Automatic Optic Disc Detection in Color Retinal Images by Local Feature Spectrum Analysis

Zhou

Wu²,

et al. 2018

Computational and Mathematical Methods in Medicine

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The optic disc is a key anatomical structure in retinal images. The ability to detect optic discs in retinal images plays an important role in automated screening systems. Inspired by the fact that humans can find optic discs in retinal images by observing some local features, we propose a local feature spectrum analysis (LFSA) that eliminates the influence caused by the variable spatial positions of local features. In LFSA, a dictionary of local features is used to reconstruct new optic disc candidate images, and the utilization frequencies of every atom in the dictionary are considered as a type of “spectrum” that can be used for classification. We also employ the sparse dictionary selection approach to construct a compact and representative dictionary. Unlike previous approaches, LFSA does not require the segmentation of vessels, and its method of considering the varying information in the retinal images is both simple and robust, making it well-suited for automated screening systems. Experimental results on the largest publicly available dataset indicate the effectiveness of our proposed approach.

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“…Each sample is given a weight that reflects its representational ability. The most representative samples are selected as dictionary atoms [23–28]. …”

Section: The Proposed Methodsmentioning

confidence: 99%

Automatic Optic Disc Detection in Color Retinal Images by Local Feature Spectrum Analysis

Zhou

Wu²,

et al. 2018

Computational and Mathematical Methods in Medicine

Self Cite

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“…In essence, FS is a dimensionality reduction process, and the features themselves do not change. A good FS algorithm can effectively reduce the original feature set dimension, has low computational complexity, and can improve the effectiveness of subsequent classification and clustering [31]. Similar to FS, FE also reduces the dimension of the original feature set.…”

Section: A Feature Engineeringmentioning

confidence: 99%

Unsupervised Detection of Abnormal Electricity Consumption Behavior Based on Feature Engineering

Zhang¹,

Dong²,

Li³

et al. 2020

IEEE Access

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The detection of abnormal electricity consumption behavior has been of great importance in recent years. However, existing research often focuses on algorithm improvement and ignores the process of obtaining features. The optimal feature set, which reflects customers' electricity consumption behavior, has a significant influence on the final detection results. Moreover, it is not straightforward to obtain datasets with label information. In this paper, a method based on feature engineering for unsupervised detection of abnormal electricity consumption behavior is proposed. First, the original feature set is constructed by brainstorming in the feature engineering step. Then, the optimal feature set, which reflects the customers' electricity consumption behavior, is obtained by features selected based on the variance and similarity between them. After that, in the abnormal detection step, a density-based clustering algorithm, in which the best clustering parameters are selected through iteration and evaluation, combined with unsupervised clustering evaluation indexes, is used to detect abnormal electricity consumption behaviors. Finally, using the load dataset of an industrial park, several typical feature strategies are applied for comparison with the feature engineering proposed in this paper. To perform the evaluation, the label information of abnormal behaviors is obtained by combining the original electricity consumption behavior detection results with abnormal data injections. The abnormal detection method proposed has given good results and outperformed typical feature strategies in an effective and generalizable way.

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“…Another line of work [23]- [32] is to select those features which can be used to well reconstruct or approximate the whole data set. Besides, it has also been verified that the local structure of data is also vital important for unsupervised features selection [16], [33]- [35]. Most recently, several techniques have also been introduced to further improve unsupervised feature selection, such as the adaptive graph learning [36], [37], the ensemble of weak partitions [38].…”

Section: Related Work a Unsupervised Feature Selectionmentioning

confidence: 99%

Unsupervised Dual Learning for Feature and Instance Selection

Ren

Zhou

et al. 2020

IEEE Access

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Feature selection and instance selection are dual operations on a data matrix. Feature selection aims at selecting a subset of relevant and informative features from original feature space, while instance selection at identifying a subset of informative and representative instances. Most of previous studies address these two problems separately, such that irrelevant features (resp. outliers) may mislead the process of instance (resp. feature) selection. In this paper, we address the problem by doing feature and instance selection simultaneously. We propose a novel unified framework, which chooses instances and features simultaneously, such that 1)all the data can be reconstructed from the selected instances and features and 2) the global structure which is characterized by the sparse reconstruction coefficient is preserved. Experimental results on several benchmark data sets demonstrate the effectiveness of our proposed method.

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Structure Preserving Non-negative Feature Self-Representation for Unsupervised Feature Selection

Cited by 32 publications

References 21 publications

Automatic Optic Disc Detection in Color Retinal Images by Local Feature Spectrum Analysis

Automatic Optic Disc Detection in Color Retinal Images by Local Feature Spectrum Analysis

Unsupervised Detection of Abnormal Electricity Consumption Behavior Based on Feature Engineering

Unsupervised Dual Learning for Feature and Instance Selection

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