A spatial-temporal linear feature learning algorithm for P300-based brain-computer interfaces by Seyedeh Nadia Aghili, Sepideh Kilani, Rami N Khushaba, Ehsan Rouhani

“…The novel spatial-temporal linear feature learning (STLFL) algorithm is proposed to extract high-level P300 features. …”
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Robust multitask feature learning for amnestic mild cognitive impairment diagnosis based on multidimensional surface measures by Leiming Jin, Xuetong Wang, Pengbo Jiang, Qiongling Li, Debin Zeng, Shuyu Li

“…Considering the actual situation that we do not know the correlation between tasks in advance, we use a robust multitask feature learning (rMTFL) method to select a group of features among correlated measures and provide additional information by identifying outlier tasks at the same time. …”
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Aircraft Type Recognition in Remote Sensing Images Based on Feature Learning with Conditional Generative Adversarial Networks by Yuhang Zhang, Hao Sun, Jiawei Zuo, Hongqi Wang, Guangluan Xu, Xian Sun

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A Deep Feature Learning Method for Drill Bits Monitoring Using the Spectral Analysis of the Acoustic Signals by Caleb Vununu, Kwang-Seok Moon, Suk-Hwan Lee, Ki-Ryong Kwon

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A Discriminative Feature Learning Approach With Distinguishable Distance Metrics for Remote Sensing Image Classification and Retrieval by Zhiqi Zhang, Wen Lu, Xiaoxiao Feng, Jinshan Cao, Guangqi Xie

“…For better CBIR performance, we propose a discriminative feature learning approach with distinguishable distance metrics for remote sensing image classification and retrieval. …”
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Rotation-Invariant Feature Learning for Object Detection in VHR Optical Remote Sensing Images by Double-Net by Zhi Zhang, Ruoqiao Jiang, Shaohui Mei, Shun Zhang, Yifan Zhang

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Stereo Feature Learning Based on Attention and Geometry for Absolute Hand Pose Estimation in Egocentric Stereo Views by Kyeongeun Seo, Hyeonjoong Cho, Daewoong Choi, Taewook Heo

“…In particular, for hand pose estimation with a stereo camera, we propose an attention-based architecture called <italic>StereoNet</italic>, a geometry-based loss function called <italic>StereoLoss</italic>, and a novel 2D disparity map called <italic>StereoDMap</italic> for effective stereo feature learning. To collect the dataset, we proposed a novel annotation method that helps reduce human annotation efforts. …”
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Multichannel False-target Discrimination in SAR Images Based on Sub-aperture and Full-aperture Feature Learning by Lin MA, Zongxu PAN, Zhongling HUANG, Bing HAN, Yuxin HU, Xiao ZHOU, Bin LEI

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Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition Using Hybrid Deep Neural Architecture by E. Rajalakshmi, R. Elakkiya, V. Subramaniyaswamy, L. Prikhodko Alexey, Grif Mikhail, Maxim Bakaev, Ketan Kotecha, Lubna Abdelkareim Gabralla, Ajith Abraham

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Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification by Ronald Tombe, Serestina Viriri

“…This article proposes the adaptive deep co-accordance feature learning (ADCFL). The ADCFL method utilizes a convolutional neural network to extract spatial feature information from an image in a co-occurrence manner with filters, and then this information is fed to the multigrain forest for feature learning and classification through majority votes with ensemble classifiers. …”
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Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review by Naftaly Wambugu, Yiping Chen, Zhenlong Xiao, Kun Tan, Mingqiang Wei, Xiaoxue Liu, Jonathan Li

“…To stimulate further research, this paper reviews current methods that handle labeled data insufficiency and the current feature learning methods for HSI classification using DCNNs. …”
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Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. by Thomas A Lasko, Joshua C Denny, Mia A Levy

“…While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don't think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features) that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. …”
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Semantic Segmentation of Very-High-Resolution Remote Sensing Images via Deep Multi-Feature Learning by Yanzhou Su, Jian Cheng, Haiwei Bai, Haijun Liu, Changtao He

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Two‐stage short‐term wind power forecasting algorithm using different feature-learning models by Jiancheng Qin, Jin Yang, Ying Chen, Qiang Ye, Hua Li

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An Attention-Enhanced End-to-End Discriminative Network With Multiscale Feature Learning for Remote Sensing Image Retrieval by Dongyang Hou, Siyuan Wang, Xueqing Tian, Huaqiao Xing

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HATF: Multi-Modal Feature Learning for Infrared and Visible Image Fusion via Hybrid Attention Transformer by Xiangzeng Liu, Ziyao Wang, Haojie Gao, Xiang Li, Lei Wang, Qiguang Miao

“…To alleviate the above problems, a framework for multimodal feature learning fusion using a cross-attention Transformer is proposed. …”
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Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images by Masoumeh Rahmani, Gholamreza Akbarizadeh

Subjects: “…unsupervised feature learning…”
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Image feature learning combined with attention‐based spectral representation for spatio‐temporal photovoltaic power prediction by Xingchen Guo, Jing Lai, Zhou Zheng, Chenxiang Lin, Yuxing Dai, Xuexin Xu, Haisheng San, Rong Jia, Zhihong Zhang

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DETECTION OF HARBOURS FROM HIGH RESOLUTION REMOTE SENSING IMAGERY VIA SALIENCY ANALYSIS AND FEATURE LEARNING by Y. Wang, L. Pan, D. Wang, Y. Kang

“…Next, SIFT features are extracted and feature learning is processed to help represent the ROIs. …”
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Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep Sparse Residual Model by Redha Touati, Max Mignotte, Mohamed Dahmane

“…In this article, we propose a novel and simple automatic model based on multimodal anomaly feature learning in a residual space, aiming at solving the binary classification problem of temporal change detection (CD) between pairs of heterogeneous remote sensing images. …”
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