Unsupervised feature selection by regularized matrix factorization

Qi, Miao; Wang, Ting; Liu, Fucong; Zhang, Baoxue; Wang, Jianzhong; Yi, Yugen

doi:10.1016/j.neucom.2017.08.047

Cited by 52 publications

(21 citation statements)

References 12 publications

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“…Qi et al [87] in 2017 proposed a novel method called Regularized Matrix Factorization Feature Selection (RMFFS). Matrix factorization determines the correlation among features.…”

Section: Graph-based Unsupervised Fsmentioning

confidence: 99%

A Review of Feature Selection and Its Methods

Venkatesh

Anuradha

2019

Cybernetics and Information Technologies

347

169

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Nowadays, being in digital era the data generated by various applications are increasing drastically both row-wise and column wise; this creates a bottleneck for analytics and also increases the burden of machine learning algorithms that work for pattern recognition. This cause of dimensionality can be handled through reduction techniques. The Dimensionality Reduction (DR) can be handled in two ways namely Feature Selection (FS) and Feature Extraction (FE). This paper focuses on a survey of feature selection methods, from this extensive survey we can conclude that most of the FS methods use static data. However, after the emergence of IoT and web-based applications, the data are generated dynamically and grow in a fast rate, so it is likely to have noisy data, it also hinders the performance of the algorithm. With the increase in the size of the data set, the scalability of the FS methods becomes jeopardized. So the existing DR algorithms do not address the issues with the dynamic data. Using FS methods not only reduces the burden of the data but also avoids overfitting of the model.

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“…Qi et al [87] in 2017 proposed a novel method called Regularized Matrix Factorization Feature Selection (RMFFS). Matrix factorization determines the correlation among features.…”

Section: Graph-based Unsupervised Fsmentioning

confidence: 99%

A Review of Feature Selection and Its Methods

Venkatesh

Anuradha

2019

Cybernetics and Information Technologies

347

169

View full text Add to dashboard Cite

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“…In the future, Locality Sensitive Hashing [46] and feature selection [47,48,49,50] will be considered to cut down the computation complexity and memory consumption. Meanwhile, Query Expansion (QE) [51] and Graph Fusion (GF) [52] will be integrated into the image retrieval system to retrieve more target images.…”

Section: Discussionmentioning

confidence: 99%

Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval

Feng

Hao

Sbert

et al. 2019

Sensors

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Riding the wave of visual sensor equipment (e.g., personal smartphones, home security cameras, vehicle cameras, and camcorders), image retrieval (IR) technology has received increasing attention due to its potential applications in e-commerce, visual surveillance, and intelligent traffic. However, determining how to design an effective feature descriptor has been proven to be the main bottleneck for retrieving a set of images of interest. In this paper, we first construct a six-layer color quantizer to extract a color map. Then, motivated by the human visual system, we design a local parallel cross pattern (LPCP) in which the local binary pattern (LBP) map is amalgamated with the color map in “parallel” and “cross” manners. Finally, to reduce the computational complexity and improve the robustness to image rotation, the LPCP is extended to the uniform local parallel cross pattern (ULPCP) and the rotation-invariant local parallel cross pattern (RILPCP), respectively. Extensive experiments are performed on eight benchmark datasets. The experimental results validate the effectiveness, efficiency, robustness, and computational complexity of the proposed descriptors against eight state-of-the-art color texture descriptors to produce an in-depth comparison. Additionally, compared with a series of Convolutional Neural Network (CNN)-based models, the proposed descriptors still achieve competitive results.

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“…Based on training data (labeled or unlabeled), feature selection methods can be classified into three categories: supervised [1,2] , semi-supervised [3,4] and unsupervised [5,6,7,8]. Moreover, based on their relationship with learning models, they can be divided into three categories: wrapper, filter and embedded methods [9,10].…”

Section: Introductionmentioning

confidence: 99%

Deep feature selection using a teacher-student network

et al. 2020

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High-dimensional data in many machine learning applications leads to computational and analytical complexities. Feature selection provides an effective way for solving these problems by removing irrelevant and redundant features, thus reducing model complexity and improving accuracy and generalization capability of the model. In this paper, we present a novel teacher-student feature selection (TSFS) method in which a 'teacher' (a deep neural network or a complicated dimension reduction method) is first employed to learn the best representation of data in low dimension. Then a 'student' network (a simple neural network) is used to perform feature selection by minimizing the reconstruction error of low dimensional representation. Although the teacher-student scheme is not new, to the best of our knowledge, it is the first time that this scheme is employed for feature selection. The proposed TSFS can be used for both supervised and unsupervised feature selection. This method is evaluated on different datasets and is compared with state-of-the-art existing feature selection methods. The results show that TSFS performs better in terms of classification and clustering accuracies and reconstruction error. Moreover, experimental evaluations demonstrate a low degree of sensitivity to parameter selection in the proposed method.

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Unsupervised feature selection by regularized matrix factorization

Cited by 52 publications

References 12 publications

A Review of Feature Selection and Its Methods

A Review of Feature Selection and Its Methods

Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval

Deep feature selection using a teacher-student network

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