In recent years, unsupervised feature selection methods have raised considerable interest in many research areas; this is mainly due to their ability to identify and select relevant features without needing class label information. In this paper, we provide a comprehensive and structured review of the most relevant and recent unsupervised feature selection methods reported in the literature. We present a taxonomy of these methods and describe the main characteristics and the fundamental ideas they are based on. Additionally, we summarized the advantages and disadvantages of the general lines in which we have categorized the methods analyzed in this review. Moreover, an experimental comparison among the most representative methods of each approach is also presented. Finally, we discuss some important open challenges in this research area.

A review of unsupervised feature selection methods

To uncover an appropriate latent subspace for data representation, in this paper we propose a novel Robust Structured Subspace Learning (RSSL) algorithm by integrating image understanding and feature learning into a joint learning framework. The learned subspace is adopted as an intermediate space to reduce the semantic gap between the low-level visual features and the high-level semantics. To guarantee the subspace to be compact and discriminative, the intrinsic geometric structure of data, and the local and global structural consistencies over labels are exploited simultaneously in the proposed algorithm. Besides, we adopt the $\ell _{2,1}$ -norm for the formulations of loss function and regularization respectively to make our algorithm robust to the outliers and noise. An efficient algorithm is designed to solve the proposed optimization problem. It is noted that the proposed framework is a general one which can leverage several well-known algorithms as special cases and elucidate their intrinsic relationships. To validate the effectiveness of the proposed method, extensive experiments are conducted on diversity datasets for different image understanding tasks, i.e., image tagging, clustering, and classification, and the more encouraging results are achieved compared with some state-of-the-art approaches.

Robust Structured Subspace Learning for Data Representation

Many pattern analysis and data mining problems have witnessed high-dimensional data represented by a large number of features, which are often redundant and noisy. Feature selection is one main technique for dimensionality reduction that involves identifying a subset of the most useful features. In this paper, a novel unsupervised feature selection algorithm, named clustering-guided sparse structural learning (CGSSL), is proposed by integrating cluster analysis and sparse structural analysis into a joint framework and experimentally evaluated. Nonnegative spectral clustering is developed to learn more accurate cluster labels of the input samples, which guide feature selection simultaneously. Meanwhile, the cluster labels are also predicted by exploiting the hidden structure shared by different features, which can uncover feature correlations to make the results more reliable. Row-wise sparse models are leveraged to make the proposed model suitable for feature selection. To optimize the proposed formulation, we propose an efficient iterative algorithm. Finally, extensive experiments are conducted on 12 diverse benchmarks, including face data, handwritten digit data, document data, and biomedical data. The encouraging experimental results in comparison with several representative algorithms and the theoretical analysis demonstrate the efficiency and effectiveness of the proposed algorithm for feature selection.

Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection

Principal component analysis (PCA) has been widely applied in the area of computer science. It is well-known that PCA is a popular transform method and the transform result is not directly related to a sole feature component of the original sample. However, in this paper, we try to apply principal components analysis (PCA) to feature selection. The proposed method well addresses the feature selection issue, from a viewpoint of numerical analysis. The analysis clearly shows that PCA has the potential to perform feature selection and is able to select a number of important individuals from all the feature components. Our method assumes that different feature components of original samples have different effects on feature extraction result and exploits the eigenvectors of the covariance matrix of PCA to evaluate the significance of each feature component of the original sample. When evaluating the significance of the feature components, the proposed method takes a number of eigenvectors into account. Then it uses a reasonable scheme to perform feature selection. The devised algorithm is not only subject to the nature of PCA but also computationally efficient. The experimental results on face recognition show that when the proposed method is able to greatly reduce the dimensionality of the original samples, it also does not bring the decrease in the recognition accuracy.

Feature Selection Using Principal Component Analysis

Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research.

Methods for artifact detection and removal from scalp EEG: A review

The brain-machine interface (BMI) has been reported to offer the potential for controlling the assistive robot for the motor impaired people, using the non-invasively obtained electroencephalogram (EEG) signals. However, the EEG based BMI may not be sufficient and stable to drive the robot moving freely in its 2D or 3D workspace. The robot autonomy may provide assistance for the BMI users with the shared control paradigm. Nevertheless, users suffers from several limitations of the current shared control paradigms applied on BMI, e.g., loss of sense of control, high mental workload due to unintuitive control with the human-robot interface and fixed level of assistance. To overcome these drawbacks, we propose a new control paradigm for the robotic arm reaching task where the robot autonomy is dynamically blended with the gaze-BMI control from a user. In this paradigm, the hybrid gaze-BMI constitutes an intuitive and effective input to continuously control the robotic arm end-effector moving freely in its 2D workspace, with an adjustable speed proportional to the motion intention strength. Furthermore, the adjustable level of assistance by our paradigm allows the system to balance the user's capabilities and feelings of control while compensating for the reaching task's difficulty. The proposed paradigm is verified in the task where a healthy subject utilizes the hybrid gaze-BMI to control the robotic arm end-effector reaching for a target object while avoiding the obstacle in the path. The experimental results demonstrate that the movements with our shared control paradigm are safer, more efficient and less difficult than those without shared control.

Continuous Shared Control for Robotic Arm Reaching Driven by a Hybrid Gaze-Brain Machine Interface

This paper proposes an algorithm to deal with the feature selection in Gaussian mixture clustering by an iterative way: the algorithm iterates between the clustering and the unsupervised feature selection. First, we propose a quantitative measurement of the feature relevance with respect to the clustering. Then, we design the corresponding feature selection scheme and integrate it into the rival penalized EM (RPEM) clustering algorithm (Cheung, 2005) that is able to determine the number of clusters automatically. Subsequently, the clustering can be performed in an appropriate feature subset by gradually eliminating the irrelevant features with automatic model selection. Compared to the existing methods, the numerical experiments have shown the efficacy of the proposed algorithm on the synthetic and real world data.

https://www.comp.hkbu.edu.hk/~ymc/papers/conference/ijcnn07_final_version.pdf

Iterative Feature Selection in Gaussian Mixture Clustering with Automatic Model Selection

This paper is to identify the clustering structure and the relevant features automatically and simultaneously in the context of Gaussian mixture model. We perform this task by introducing two sets of weight functions under the recently proposed Maximum Weighted Likelihood (MWL) learning framework. One set is to reward the significance of each component in the mixture, and the other one is to discriminate the relevance of each feature to the cluster structure. The experiments on both the synthetic and real-world data show the efficacy of the proposed algorithm.

/pdf/a-maximum-weighted-likelihood-approach-to-simultaneous-model-3btpwbf6sw.pdf

A maximum weighted likelihood approach to simultaneous model selection and feature weighting in Gaussian mixture

Measuring regolith density in outer space has always been a difficult problem. In this paper, a bulk density measuring method for planetary regolith based on vibration is proposed. The bulk density measurement of planetary regolith being sampled is realized mainly by vibration signal processing and general regression neural network identification. The method makes full use of the special structure of a mini coiling-type sampler, which utilizes the flexible coiling spring as its sampling arm. To begin, vibration signals are acquired and their trends are removed. Then, multiresolution wavelet analysis is used to filter out the signals that are not related to bulk density. After that, two of the power spectrum estimation methods, Periodogram and Welch, are introduced to extract the density-related features of the vibration signals and reduce the dimensions of them. The multiclass classification support vector machine is then adopted to verify the relationship between the bulk densities of regoliths a...

In Situ Regolith Bulk Density Measurement for a Coiling-Type Sampler

Capturing finger joint angle information has important applications in human–computer interaction and hand function evaluation. In this paper, a novel wearable data glove is proposed for capturing finger joint angles. A sensing unit based on a grating strip and an optical detector is specially designed for finger joint angle measurement. To measure the angles of finger joints, 14 sensing units are arranged on the back of the glove. There is a sensing unit on the back of each of the middle phalange, proximal phalange, and metacarpal of each finger, except for the thumb. For the thumb, two sensing units are distributed on the back of the proximal phalange and metacarpal, respectively. Sensing unit response tests and calibration experiments are conducted to evaluate the feasibility of using the designed sensing unit for finger joint measurement. Experimental results of calibration show that the comprehensive precision of measuring the joint angle of a wooden finger model is 1.67%. Grasping tests and static digital gesture recognition experiments are conducted to evaluate the performance of the designed glove. We achieve a recognition accuracy of 99% by using the designed glove and a generalized regression neural network (GRNN). These preliminary experimental results indicate that the designed data glove is effective in capturing finger joint angles.

https://www.mdpi.com/2072-666X/12/7/771/pdf

Hong Zeng

Papers

Continuous Shared Control for Robotic Arm Reaching Driven by a Hybrid Gaze-Brain Machine Interface

Iterative Feature Selection in Gaussian Mixture Clustering with Automatic Model Selection

A maximum weighted likelihood approach to simultaneous model selection and feature weighting in Gaussian mixture

In Situ Regolith Bulk Density Measurement for a Coiling-Type Sampler

Development of a Low-Cost Wearable Data Glove for Capturing Finger Joint Angles.