Convolutional variational autoencoder-based feature learning for automatic tea clone recognition by Vicky Zilvan, Ade Ramdan, Ana Heryana, Dikdik Krisnandi, Endang Suryawati, R. Sandra Yuwana, R. Budiarianto S. Kusumo, Hilman F. Pardede

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DFL-LC: Deep Feature Learning With Label Consistencies for Hyperspectral Image Classification by Siyuan Liu, Yun Cao, Yuebin Wang, Junhuan Peng, P. Takis Mathiopoulos, Yong Li

“…Aiming to extract more representative and discriminative image features, in this article, the deep feature learning with label consistencies (DFL-LC) method is developed to realize HSI classification. …”
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Feature Learning for SAR Target Recognition with Unknown Classes by Using CVAE-GAN by Xiaowei Hu, Weike Feng, Yiduo Guo, Qiang Wang

Subjects: “…feature learning…”
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Tuning the Weights: The Impact of Initial Matrix Configurations on Successor Features’ Learning Efficacy by Hyunsu Lee

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Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions by Xiaomei Bai, Fuli Zhang, Jie Hou, Feng Xia, Amr Tolba, Elsayed Elashkar

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Semi-Supervised Ground-to-Aerial Adaptation with Heterogeneous Features Learning for Scene Classification by Zhipeng Deng, Hao Sun, Shilin Zhou

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End-to-End Light Field Image Compression with Multi-Domain Feature Learning by Kangsheng Ye, Yi Li, Ge Li, Dengchao Jin, Bo Zhao

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Discriminative feature learning‐based pixel difference representation for facial expression recognition by Zhe Sun, Zheng‐Ping Hu, Meng Wang, Shu‐Huan Zhao

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Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery by Ignacio Ponzoni, Víctor Sebastián-Pérez, Carlos Requena-Triguero, Carlos Roca, María J. Martínez, Fiorella Cravero, Mónica F. Díaz, Juan A. Páez, Ramón Gómez Arrayás, Javier Adrio, Nuria E. Campillo

“…Two main general approaches can be used for this modeling procedure: feature selection and feature learning. In this paper, a performance comparative study of two state-of-art methods related to these two approaches is carried out. …”
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Music recommendation algorithms based on knowledge graph and multi-task feature learning by Xinqiao Liu, Zhisheng Yang, Jinyong Cheng

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Low‐rank constrained weighted discriminative regression for multi‐view feature learning by Chao Zhang, Huaxiong Li

“…A new supervised feature learning method for multi‐view data, called low‐rank constrained weighted discriminative regression (LWDR), is proposed. …”
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Identifying indicator species in ecological habitats using Deep Optimal Feature Learning. by Yiting Tsai, Susan A Baldwin, Bhushan Gopaluni

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Simultaneous local binary feature learning and encoding for homogeneous and heterogeneous face recognition by Lu, Jiwen, Liong, Venice Erin, Zhou, Jie

“…In this paper, we propose a simultaneous local binary feature learning and encoding (SLBFLE) approach for both homogeneous and heterogeneous face recognition. …”
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Exemplar based deep discriminative and shareable feature learning for scene image classification by Zuo, Zhen, Wang, Gang, Shuai, Bing, Zhao, Lifan, Yang, Qingxiong

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Unsupervised Feature Learning With Graph Embedding for View-Based 3D Model Retrieval by Yuting Su, Wenhui Li, Weizhi Nie, Dan Song, An-An Liu

“…For this challenging task, feature learning and similarity measure are two critical problems. …”
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Optimized Feature Learning for Anti-Inflammatory Peptide Prediction Using Parallel Distributed Computing by Salman Khan, Muhammad Abbas Khan, Mukhtaj Khan, Nadeem Iqbal, Salman A. AlQahtani, Mabrook S. Al-Rakhami, Dost Muhammad Khan

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ReFPN-FCOS: One-Stage Object Detection for Feature Learning and Accurate Localization by Jiexian Zeng, Jiale Xiong, Xiang Fu, Lu Leng

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Cross-Task Fault Diagnosis Based on Deep Domain Adaptation With Local Feature Learning by Ying Tian, Yin Tang, Xin Peng

“…Moreover, to further optimize the model, we propose the local feature learning, which can make test data with better intra-class compactness and inter-class separability. …”
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Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine by Xiaoping Fang, Yaoming Cai, Zhihua Cai, Xinwei Jiang, Zhikun Chen

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Classification Method of Voltage Sag Sources Based on Sequential Trajectory Feature Learning Algorithm by Zhang Yikun, He Yingjie, Zhang Haixiao, Li Jiahao, Li Yijin, Liu Jinjun

“…Therefore, this article proposes an effective and interpretable voltage sag sources classification method based on sequential trajectory feature learning and Random Forest algorithm. Firstly, to fully consider the interpretable sequential trajectory features of voltage sag signals so as to improve the quality and interpretability of calculation process, the fused lasso generalized eigenvector (FLAG) algorithm is adopted to quickly search for interpretable shapelets sub-sequences from the labeled voltage sag data. …”
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