Pattern recognition (psychology)

About: Pattern recognition (psychology) is a research topic. Over the lifetime, 26182 publications have been published within this topic receiving 722805 citations. The topic is also known as: physiological pattern recognition & Pattern Recognition, Physiological.

An expert system for automated classification of phases in cyclic alternating patterns of sleep using optimal wavelet‐based entropy features

Abstract: Humans spend a significant portion of their time in the state of sleep, and therefore one's’sleep health’ is an important indicator of the overall health of an individual. Non-invasive methods such as electroencephalography (EEG) are used to evaluate the ’sleep health’ as well as associated disorders such as nocturnal front lobe epilepsy, insomnia, and narcolepsy. A long-duration and repetitive activity, known as a cyclic alternating pattern (CAP), is observed in the EEG waveforms which reflect the cortical electrical activity during non-rapid eye movement (NREM) sleep. The CAP sequences involve various, continuing periods of phasic activation (phase-A) and deactivation (phase-B). The manual analysis of these signals performed by clinicians are prone to errors, and may lead to the wrong diagnosis. Hence, automated systems that can classify the two phases (viz. Phase A and Phase B accurately can eliminate any human involvement in the diagnosis. The pivotal aim of this study is to evaluate the usefulness of stopband energy minimized biorthogonal wavelet filter bank (BOWFB) based entropy features in the identification of CAP phases. We have employed entropy features obtained from six wavelet subbands of EEG signals to develop a machine learning (ML) based model using various supervised ML algorithms. The proposed model by us yielded an average classification accuracy of 74.40% with 10% hold-out validation with the balanced dataset, and maximum accuracy of 87.83% with the unbalanced dataset using ensemble bagged tree classifier. The developed expert system can assist the medical practitioners to assess the person's cerebral activity and quality of sleep accurately.

Multibranch Feature Fusion Network With Self- and Cross-Guided Attention for Hyperspectral and LiDAR Classification

Abstract: The effective fusion of multisource data helps to improve the performance of land cover classification. Most existing convolutional neural network (CNN)-based methods adopt an early/late fusion strategy to fuse the low-/high-level features for classification, which still has two inherent challenges: 1) the conventional convolution operation performs a weighted average operation on each pixel in the receptive field, which will reduce the discriminability of the center pixel due to the influence of the interference pixels and 2) the spatial–spectral features of the hyperspectral image (HSI), the elevation features of light detection and ranging (LiDAR), and the complementary features between the multimodal data are not fully exploited, which results in the reduction of classification accuracy. In this article, an effective multibranch feature fusion network with self- and cross-guided attention (MB2FscgaNet) is proposed for the joint classification of LiDAR and HSI. The main concern of this article is how to accurately estimate more effective spectral–spatial-elevation features and yield more effective transfer in the network. Specifically, MB2FscgaNet adopts a multibranch feature fusion architecture to fully exploit the hierarchical features from LiDAR and HSI level by level. At each level of the network, a self- and cross-guided attention (SCGA) is developed to assign a higher weight to interesting areas and channels of LiDAR and HSI feature maps to obtain refined spectral–spatial-elevation features and provide complementary information cross-guidance between LiDAR and HS. We further designed a spectral supplement module (SeSuM) to improve the discriminative ability of the center pixel. Comparative classification results and ablation studies demonstrate that the proposed MB2FscgaNet achieves competitive performance against state-of-the-art methods.

Two‐stage‐neighborhood‐based multilabel classification for incomplete data with missing labels

Abstract: In recent years, it has been difficult for multilabel classification to obtain complete multilabel data in real‐world applications, and even a large number of labels for training samples are randomly missed. As a result, the classification task of incomplete multilabel data with missing labels faces formidable challenges. This paper presents a two‐stage‐neighborhood‐based multilabel classification method for incomplete data with missing labels in neighborhood decision systems. First, to solve the problem of selecting the neighborhood radius manually, as well as balancing the samples in the neighborhood, the neighborhood radius based on the feature distribution function is defined, and the differences and similarities between samples through the identifiable and indiscernible matrices are, respectively, computed. Then, a restoration method for missing feature values is proposed for use in the first stage. Second, to consider the nonlinear relationship among features, a neighborhood‐based fuzzy similarity relationship between samples is investigated based on the Gaussian kernel function. By integrating the fuzzy similarity relationship matrix, label‐specific feature matrix, and label correlation matrix, an objective function based on the regression model is presented, the optimal solutions to the label‐specific feature and label correlation matrices based on the gradient descent strategy are provided, and a new multilabel classification method with missing labels is developed during the second stage. Finally, two‐stage multilabel classification algorithms are designed. Experiments on 18 multilabel data sets demonstrate that our designed algorithms are effective not only for recovering missing feature values, but also for improving the classification performance of data with missing labels.

Distilling Knowledge From an Ensemble of Convolutional Neural Networks for Seismic Fault Detection

Abstract: Fault detection is a crucial task in seismic structure interpretation. Convolutional neural network (CNN)-based methods, in general, require large amount of labeled data for network training. One way to build the labeled data is to create synthetic seismic images with corresponding fault labels. However, it is hard to ensure that the synthetic data have the same fault feature distributions as the field data, which may lead to inaccurate and unreliable prediction results. Another way is to manually label the faults, which is time-consuming and subjective. In this letter, we propose that using knowledge distillation (KD) to improve the performance of fault detection by integrating the features from large number of synthetic samples and a small number of field samples. We distill knowledge from an ensemble of two teacher CNNs to train a student CNN (applied to final target) for seismic fault detection. In our work, one segmentation teacher CNN is trained on synthetic samples with known ground truth fault labels and another classification teacher CNN is trained on field samples with manually picked labels. Then, a classification student network is trained on samples generated by voting the results from two teacher models. The student CNN learns not only the general fault characteristics in the synthetic data but also the specific fault features of the target field data. Test on the field data shows that the student CNN highlights seismic fault more accurately with higher resolution than the teacher CNNs.