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

Eye tracking technology provides many advantages in human-computer interaction and human-robot interaction research. However, most studies in human-computer interaction are based on prohibitively expensive professional eye-trackers, which cannot be affordable for daily life applications. Though consumer-level low-cost eye trackers have appeared in market in recent years, they can only output raw gaze recordings and lack the high-level measurements/features for further analyzing the user’s implicit visual intention in an image. To this end, a gaze-based intention detection approach using a low cost remote eye tracker is proposed. In this study, we form the gaze point into clusters and extract gaze-based features from the clusters. Then a support vector machine (SVM) classifier is used for detecting intentional gaze clusters. According to our experiment results obtained from 15 subjects, the accuracy for classifying the gaze clusters is 97.85%. Besides, the performance of our approach for detecting user’s interested objects from the visual image of the real-life scene is comparable with the state-of-the-art method that employs an expensive professional eye tracker. The overall results suggest that our approach based on a low-cost remote eye tracker is applicable for detecting user’s interested object.

Interested Object Detection based on Gaze using Low-cost Remote Eye Tracker

Haptic devices have been applied in interactive operation to perform contact tasks. To explore the haptic perception characteristics of typical push-pull and rotation operation, an experimental system was built by incorporating a three degrees of freedom (3-DOF) haptic device and the virtual environment. In this system, the haptic device is used to provide motion commands to control the avatar in the virtual environment and to exert haptic feedback on the human operator generated by three motors. In order to evaluate the main influential factors of interactive system based on haptic devices, ergonomic assessments are designed and experimentally implemented. Preliminary studies on the factors including restoring force, guidance force, speed of the virtual avatar, and the arm length have been conducted. The results are of great significance for the design of a haptic device and haptic interaction system by analyzing the specific requirements of ergonomics.

/pdf/experiments-and-assessments-of-a-3-dof-haptic-device-for-dnojko33o3.pdf

Experiments and assessments of a 3-DOF haptic device for interactive operation

It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

https://journals.ametsoc.org/doi/pdf/10.1175/JTECH-D-13-00238.1

Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

The invention provides a humanoid hand finger tip slide tactile sensor. A rubber finger tip is a hemispherical cavity; a PVDF film is attached to the outer surface of the rubber finger tip; and a sealing plug seals the rubber finger tip, and the bottom portion of the sealing plug is equipped with a liquid pressure sensor. A housing main body is in a rigid barrel structure; and the top portion of the housing main body is provided with a circular opening, and the bottom portion thereof is a flange disc structure, which is provided with four circular through holes in symmetric distribution in theform of a cross. An inner skeleton comprises a cylinder head, a vertical strain rod and a base. All sides of the strain rod close to the base are respectively attached by a strain foil. The base is provided with four round threaded through holes in symmetric distribution in the form of a cross. Connecting bolts pass through the through holes in the bottom portion of the housing and are cooperatedwith the threaded through holes in the base respectively to enable the inner skeleton to be connected with the housing. The PVDF film and the rubber finger tip, and the rubber finger tip and the inner skeleton and the housing are respectively fixedly connected through binders. The sensor can be mounted at the finger tip of a bionical artificial hand or a mechanical arm, and is used for obtaininginformation of texture, material and form and the like of an object and contact information between the finger and the contact object.

Humanoid hand finger tip slide tactile sensor

With the advantages of comfortable wearing and outdoor usage, the myoelectric gesture recognition techniques have gained much attention in the field of human-machine interaction (HMI). The purpose of this study is to optimize model structure and transfer generalized features to improve the robustness of myoelectric hand motion decoding. We derived the hand motion recognition framework from the muscle synergy theory, which is formulated as a temporal convolutional (TC) model of array sEMG signals, then a hierarchical myoelectric decoding model was proposed to predict simultaneous and continuous hand motion. The model was trained by the methods of unsupervised low-level feature learning and automated data labeling to minimize training supervision. Extensive experiments on the public sEMG database (17 subjects in Biopatrec) show that the TC model can extract muscle synergy features with higher fidelity ( ${R}^{2} = 0.85\pm 0.23$ ) than the traditional instantaneous mixture model, the results of online test demonstrate robust myoelectric decoding on multiple simultaneous and continuous hand motions. More importantly, the analysis of weights visualization shows that the low-level feature representation layer of TC model can be migrated across the individuals, which provides a transferrable feature extraction layer for generalized hand motion decoding.

/pdf/robust-continuous-hand-motion-recognition-using-wearable-2oi6w8v2ui.pdf

Hong Zeng

Papers

Interested Object Detection based on Gaze using Low-cost Remote Eye Tracker

Experiments and assessments of a 3-DOF haptic device for interactive operation

Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

Humanoid hand finger tip slide tactile sensor

Robust Continuous Hand Motion Recognition Using Wearable Array Myoelectric Sensor