Unsupervised Feature Extraction Algorithm Research Articles

Unsupervised layer-wise feature extraction algorithm for surface electromyography based on information theory

Feature extraction is a key task in the processing of surface electromyography (SEMG) signals. Currently, most of the approaches tend to extract features with deep learning methods, and show great performance. And with the development of deep learning, in which supervised learning is limited by the excessive expense incurred due to the reliance on labels. Therefore, unsupervised methods are gaining more and more attention. In this study, to better understand the different attribute information in the signal data, we propose an information-based method to learn disentangled feature representation of SEMG signals in an unsupervised manner, named Layer-wise Feature Extraction Algorithm (LFEA). Furthermore, due to the difference in the level of attribute abstraction, we specifically designed the layer-wise network structure. In TC score and MIG metric, our method shows the best performance in disentanglement, which is 6.2 lower and 0.11 higher than the second place, respectively. And LFEA also get at least 5.8% accuracy lead than other models in classifying motions. All experiments demonstrate the effectiveness of LEFA.

An Unsupervised Feature Extraction Approach Based on Self-Expression.

Feature extraction algorithms lack good interpretability during the projection learning. To solve this problem, an unsupervised feature extraction algorithm, that is, block diagonal projection (BDP), based on self-expression is proposed. Specifically, if the original data are projected into a low-dimensional subspace by a feature extraction algorithm, although the data may be more compact, the new features obtained may not be as explanatory as the original sample features. Therefore, by imposing L2,1 norm constraint on the projection matrix, the projection matrix can be of row sparsity. On one hand, discriminative features can be selected to make the projection matrix to be more interpretable. On the other hand, irrelevant or redundant features can be suppressed. The proposed model integrates feature extraction and selection into one framework. In addition, since self-expression can well excavate the correlation between samples or sample features, the unsupervised feature extraction task can be better guided using this property between them. At the same time, the block diagonal representation regular term is introduced to directly pursue the block diagonal representation. Thus, the accuracy of pattern recognition tasks such as clustering and classification can be improved. Finally, the effectiveness of BDP in linear dimensionality reduction and classification is proved on various reference datasets. The experimental results show that this algorithm is superior to previous feature extraction counterparts.

Improved Locality Preserving Projection for Hyperspectral Image Classification in Probabilistic Framework

Unlabeled samples and transformation matrix are two main parts of unsupervised and semi-supervised feature extraction (FE) algorithms. In this manuscript, a semi-supervised FE method, locality preserving projection in the probabilistic framework (LPPPF), to find a sufficient number of reliable and unmixed unlabeled samples from all classes and constructing an optimal projection matrix is proposed. The LPPPF has two main steps. In the first step, a number of reliable unlabeled samples are selected based on the training samples, spectral features, and spatial information in the probabilistic framework. In this way, the spectral and spatial probability distribution function is calculated for each unlabeled sample. Therefore, the spectral features and spatial information are integrated together with a joint probability distribution function. Finally, a sufficient number of unlabeled samples with the highest joint probability distribution are selected. In the second step, the selected unlabeled samples are applied to construct the transformation matrix based on the spectral and spatial information of the unlabeled samples. The adjacency graph is improved by using new weights based on spectral and spatial information. This method is evaluated on three data sets: Indian Pines, Pavia University, and Kennedy Space Center (KSC) and compared with some recent and well-known supervised, semi-supervised, and unsupervised FE methods. Various experiments demonstrate the efficiency of the LPPPF in comparison with the other FE methods. LPPPF has also considerable performance with limited training samples.

Event-Based Feature Extraction Using Adaptive Selection Thresholds.

Unsupervised feature extraction algorithms form one of the most important building blocks in machine learning systems. These algorithms are often adapted to the event-based domain to perform online learning in neuromorphic hardware. However, not designed for the purpose, such algorithms typically require significant simplification during implementation to meet hardware constraints, creating trade offs with performance. Furthermore, conventional feature extraction algorithms are not designed to generate useful intermediary signals which are valuable only in the context of neuromorphic hardware limitations. In this work a novel event-based feature extraction method is proposed that focuses on these issues. The algorithm operates via simple adaptive selection thresholds which allow a simpler implementation of network homeostasis than previous works by trading off a small amount of information loss in the form of missed events that fall outside the selection thresholds. The behavior of the selection thresholds and the output of the network as a whole are shown to provide uniquely useful signals indicating network weight convergence without the need to access network weights. A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations. The use of selection thresholds is shown to produce network activation patterns that predict classification accuracy allowing rapid evaluation and optimization of system parameters without the need to run back-end classifiers. The feature extraction method is tested on both the N-MNIST (Neuromorphic-MNIST) benchmarking dataset and a dataset of airplanes passing through the field of view. Multiple configurations with different classifiers are tested with the results quantifying the resultant performance gains at each processing stage.

Scalable end-to-end recurrent neural network for variable star classification

ABSTRACT During the last decade, considerable effort has been made to perform automatic classification of variable stars using machine-learning techniques. Traditionally, light curves are represented as a vector of descriptors or features used as input for many algorithms. Some features are computationally expensive, cannot be updated quickly and hence for large data sets such as the LSST cannot be applied. Previous work has been done to develop alternative unsupervised feature extraction algorithms for light curves, but the cost of doing so still remains high. In this work, we propose an end-to-end algorithm that automatically learns the representation of light curves that allows an accurate automatic classification. We study a series of deep learning architectures based on recurrent neural networks and test them in automated classification scenarios. Our method uses minimal data pre-processing, can be updated with a low computational cost for new observations and light curves, and can scale up to massive data sets. We transform each light curve into an input matrix representation whose elements are the differences in time and magnitude, and the outputs are classification probabilities. We test our method in three surveys: OGLE-III, Gaia, and WISE. We obtain accuracies of about $95{{\ \rm per\ cent}}$ in the main classes and $75{{\ \rm per\ cent}}$ in the majority of subclasses. We compare our results with the Random Forest classifier and obtain competitive accuracies while being faster and scalable. The analysis shows that the computational complexity of our approach grows up linearly with the light-curve size, while the traditional approach cost grows as Nlog (N).

Unsupervised Nonnegative Adaptive Feature Extraction for Data Representation

In this paper, we propose a novel unsupervised Nonnegative Adaptive Feature Extraction (NAFE) algorithm for data representation and classification. The formulation of NAFE integrates the sparsity constrained nonnegative matrix factorization (NMF), representation learning, and adaptive reconstruction weight learning into a unified model. Specifically, NAFE performs feature and weight learning over the new robust representations of NMF for more accurate measure and representation. For nonnegative adaptive feature extraction, our NAFE first utilizes the sparsity constrained NMF to obtain the new and robust representations of the original data. To preserve the manifold structures of the learnt new representations, we also incorporate a neighborhood reconstruction error over the weight matrix for joint minimization. Note that to further improve the representation power, the weights are jointly shared in the new low-dimensional nonnegative representation space, low-dimensional nonlinear manifold space, and low-dimensional projective subspace, i.e., local neighborhood information is clearly preserved in different feature spaces so that informative representations and features can be jointly obtained. To enable NAFE to extract features from new data, we also include a feature approximation error by a linear projection so that the learnt extractor can obtain features from new data efficiently. Extensive simulations show that our formulation can deliver state-of-the-art results on several public databases for feature extraction and classification, compared with several related methods.

Unsupervised Spatial–Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification

Feature learning technologies using convolutional neural networks (CNNs) have shown superior performance over traditional hand-crafted feature extraction algorithms. However, a large number of labeled samples are generally required for CNN to learn effective features under classification task, which are hard to be obtained for hyperspectral remote sensing images. Therefore, in this paper, an unsupervised spatial–spectral feature learning strategy is proposed for hyperspectral images using 3-Dimensional (3D) convolutional autoencoder (3D-CAE). The proposed 3D-CAE consists of 3D or elementwise operations only, such as 3D convolution, 3D pooling, and 3D batch normalization, to maximally explore spatial–spectral structure information for feature extraction. A companion 3D convolutional decoder network is also designed to reconstruct the input patterns to the proposed 3D-CAE, by which all the parameters involved in the network can be trained without labeled training samples. As a result, effective features are learned in an unsupervised mode that label information of pixels is not required. Experimental results on several benchmark hyperspectral data sets have demonstrated that our proposed 3D-CAE is very effective in extracting spatial–spectral features and outperforms not only traditional unsupervised feature extraction algorithms but also many supervised feature extraction algorithms in classification application.

UFKLDA: An unsupervised feature extraction algorithm for anomaly detection under cloud environment

UFKLDA: An unsupervised feature extraction algorithm for anomaly detection under cloud environment

Approach to 3D face reconstruction through local deep feature alignment

Here, the authors propose an end‐to‐end method based on deep learning to reconstruct three‐dimensional (3D) face models from given face images. In the training stage, the authors propose to extract the feature representations from the 3D sample faces and corresponding 2D sample images through the proposed local deep feature alignment (LDFA) algorithm, and estimate an explicit mapping from the 2D features to their 3D counterparts for each local neighbourhood, then the authors learn a feed‐forward deep neural network for each neighbourhood whose parameters are initialised with the parameters obtained in the locality‐aware learning process and the explicit mapping. In the testing stage, the authors only need to feed a given face image to the deep neural network corresponding to the nearest sample image and receive the outputted 3D face model. Extensive experiments have been conducted on both non‐face and face data sets. The authors find that the LDFA algorithm performs better than several popular unsupervised feature extraction algorithms, and the 3D reconstruction results obtained by the proposed method also outperform the comparison methods.

Classification of Overt and Covert Speech for Near-Infrared Spectroscopy-Based Brain Computer Interface.

People suffering from neuromuscular disorders such as locked-in syndrome (LIS) are left in a paralyzed state with preserved awareness and cognition. In this study, it was hypothesized that changes in local hemodynamic activity, due to the activation of Broca’s area during overt/covert speech, can be harnessed to create an intuitive Brain Computer Interface based on Near-Infrared Spectroscopy (NIRS). A 12-channel square template was used to cover inferior frontal gyrus and changes in hemoglobin concentration corresponding to six aloud (overtly) and six silently (covertly) spoken words were collected from eight healthy participants. An unsupervised feature extraction algorithm was implemented with an optimized support vector machine for classification. For all participants, when considering overt and covert classes regardless of words, classification accuracy of 92.88 ± 18.49% was achieved with oxy-hemoglobin (O2Hb) and 95.14 ± 5.39% with deoxy-hemoglobin (HHb) as a chromophore. For a six-active-class problem of overtly spoken words, 88.19 ± 7.12% accuracy was achieved for O2Hb and 78.82 ± 15.76% for HHb. Similarly, for a six-active-class classification of covertly spoken words, 79.17 ± 14.30% accuracy was achieved with O2Hb and 86.81 ± 9.90% with HHb as an absorber. These results indicate that a control paradigm based on covert speech can be reliably implemented into future Brain–Computer Interfaces (BCIs) based on NIRS.

Physics-Based Image Segmentation Using First Order Statistical Properties and Genetic Algorithm for Inductive Thermography Imaging.

Thermographic inspection has been widely applied to non-destructive testing and evaluation with the capabilities of rapid, contactless, and large surface area detection. Image segmentation is considered essential for identifying and sizing defects. To attain a high-level performance, specific physics-based models that describe defects generation and enable the precise extraction of target region are of crucial importance. In this paper, an effective genetic first-order statistical image segmentation algorithm is proposed for quantitative crack detection. The proposed method automatically extracts valuable spatial-temporal patterns from unsupervised feature extraction algorithm and avoids a range of issues associated with human intervention in laborious manual selection of specific thermal video frames for processing. An internal genetic functionality is built into the proposed algorithm to automatically control the segmentation threshold to render enhanced accuracy in sizing the cracks. Eddy current pulsed thermography will be implemented as a platform to demonstrate surface crack detection. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. In addition, a global quantitative assessment index F-score has been adopted to objectively evaluate the performance of different segmentation algorithms.

Hyperspectral Image Feature Extraction Using Maclaurin Series Function Curve Fitting

Most of existing spectral-based feature extraction algorithms have gained increasing attention in hyperspectral image classification tasks. However, only original spectral is difficult to well represent or reveal intrinsic geometry structure of the image. In this paper, we construct the new features for each spectral response curve of hyperspectral image pixels, and then proposed a novel unsupervised nonlinear feature extraction algorithm that focuses on curve fitting and label-based discrimination analysis framework. In the algorithm, the coefficients of the fitted Maclaurin series function are considered as new extracted features in order to better capture the intrinsic geometrical nature of spectral response curves. Moreover, the algorithm can utilize the reflectance coefficients information of spectral response curves which has not been solved by many other statistical analysis based methods. The maximum likelihood classification results on two real-world hyperspectral image datasets have demonstrated the superiority of the proposed algorithm in image classification tasks.

Using Hurst and Lyapunov Exponent For Hyperspectral Image Feature Extraction

Hyperspectral image processing has attracted high attention in remote sensing fields. One of the main issues is to develop efficient methods for dimensionality reduction via feature extraction. This letter proposes a new nonlinear unsupervised feature extraction algorithm using Hurst and Lyapunov exponents to reveal local and general spectral profiles, respectively. A hyperspectral reflectance curve from each pixel is regarded as a time series, and it is represented by Hurst and Lyapunov exponents. These two new features are then used to overcome the Hughes problem for reliable classification. Experimental results show that the proposed method performs better than a few other feature extraction methods tested.

Unsupervised spike extraction and classification based on locality preserving projection

The spike sorting, including neuronal spike waveform acquisition and classification, is one of the important procedures in neuronal information processing, and its feature extraction and recognition are the basis of the above issues. Based on Locality Preserving Projection ( LPP) algorithm, an unsupervised feature extraction and classification algorithm was proposed. The neighbor parameter was selected automatically, the distribution dispersion standard was obtained according to the original data set, and the features of extraction results in spikes were separated effectively. The application in simulation and real experimental data show that, the proposed method based on the LPP can more effectively extract and separate features of spikes in comparison of the traditional Principle Component Analysis ( PCA) algorithm.