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学者姓名：庄一新
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Abstract ：
As a widely used method in signal processing, Principal Component Analysis (PCA) performs both the compression and the recovery of high dimensional data by leveraging the linear transformations. Considering the robustness of PCA, how to discriminate correct samples and outliers in PCA is a crucial and challenging issue. In this paper, we present a general model, which conducts PCA via a non-decreasing concave regularized minimization and is termed PCA-NCRM for short. Different from most existing PCA methods, which learn the linear transformations by minimizing the recovery errors between the recovered data and the original data in the least squared sense, our model adopts the monotonically non-decreasing concave function to enhance the ability of model in distinguishing correct samples and outliers. To be specific, PCA-NCRM enlarges the attention to samples with smaller recovery errors and diminishes the attention to samples with larger recovery errors at the same time. The proposed minimization problem can be efficiently addressed by employing an iterative re-weighting optimization. Experimental results on several datasets show the effectiveness of our model.
Keyword ：
Adaptation models Adaptation models Dimensionality reduction Dimensionality reduction High dimensional data High dimensional data Iterative algorithms Iterative algorithms Iterative re-weighting optimization Iterative re-weighting optimization Lagrangian functions Lagrangian functions Minimization Minimization Optimization Optimization Principal component analysis Principal component analysis principal component analysis (PCA) principal component analysis (PCA) Robustness Robustness Signal processing algorithms Signal processing algorithms unsupervised dimensionality reduction unsupervised dimensionality reduction
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| GB/T 7714 | Zheng, Qinghai , Zhuang, Yixin . Non-Decreasing Concave Regularized Minimization for Principal Component Analysis [J]. | IEEE SIGNAL PROCESSING LETTERS , 2025 , 32 : 486-490 . |
| MLA | Zheng, Qinghai 等. "Non-Decreasing Concave Regularized Minimization for Principal Component Analysis" . | IEEE SIGNAL PROCESSING LETTERS 32 (2025) : 486-490 . |
| APA | Zheng, Qinghai , Zhuang, Yixin . Non-Decreasing Concave Regularized Minimization for Principal Component Analysis . | IEEE SIGNAL PROCESSING LETTERS , 2025 , 32 , 486-490 . |
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Abstract ：
Neural implicit representations are highly effective for single-view 3D reconstruction (SVR). It represents 3D shapes as neural fields and conditions shape prediction on input image features. Image features can be less effective when significant variations of occlusions, views, and appearances exist from the image. To learn more robust features, we design a new feature encoding scheme that works in both image and shape space. Specifically, we present a geometry-aware 2D convolutional kernel to learn image appearance and view information along with geometric relations. The convolutional kernel operates at the 2D projections of a point-based 3D geometric structure, called spatial pattern. Furthermore, to enable the network to discover adaptive spatial patterns that capture non-local contexts, the kernel is devised to be deformable and exploited by a spatial pattern generator. Experimental results on both synthetic and real datasets demonstrate the superiority of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Keyword ：
Convolution Convolution Geometry Geometry Image reconstruction Image reconstruction
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| GB/T 7714 | Zhuang, Yixin , Wang, Yujie , Liu, Yunzhe et al. Neural Implicit 3D Shapes from Single Images with Spatial Patterns [C] . 2023 : 210-227 . |
| MLA | Zhuang, Yixin et al. "Neural Implicit 3D Shapes from Single Images with Spatial Patterns" . (2023) : 210-227 . |
| APA | Zhuang, Yixin , Wang, Yujie , Liu, Yunzhe , Chen, Baoquan . Neural Implicit 3D Shapes from Single Images with Spatial Patterns . (2023) : 210-227 . |
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Visual (re)localization addresses the problem of estimating the 6-DoF (Degree of Freedom) camera pose of a query image captured in a known scene, which is a key building block of many computer vision and robotics applications. Recent advances in structure-based localization solve this problem by memorizing the mapping from image pixels to scene coordinates with neural networks to build 2D-3D correspondences for camera pose optimization. However, such memorization requires training by amounts of posed images in each scene, which is heavy and inefficient. On the contrary, few-shot images are usually sufficient to cover the main regions of a scene for a human operator to perform visual localization. In this paper, we propose a scene region classification approach to achieve fast and effective scene memorization with few-shot images. Our insight is leveraging a) pre-learned feature extractor, b) scene region classifier, and c) meta-learning strategy to accelerate training while mitigating overfitting. We evaluate our method on both indoor and outdoor benchmarks. The experiments validate the effectiveness of our method in the few-shot setting, and the training time is significantly reduced to only a few minutes. 11Code available at: https://github.com/siyandong/SRC © 2022 IEEE.
Keyword ：
Cameras Cameras Computer vision Computer vision Degrees of freedom (mechanics) Degrees of freedom (mechanics) Learning systems Learning systems
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| GB/T 7714 | Dong, Siyan , Wang, Shuzhe , Zhuang, Yixin et al. Visual Localization via Few-Shot Scene Region Classification [C] . 2022 : 393-402 . |
| MLA | Dong, Siyan et al. "Visual Localization via Few-Shot Scene Region Classification" . (2022) : 393-402 . |
| APA | Dong, Siyan , Wang, Shuzhe , Zhuang, Yixin , Kannala, Juho , Pollefeys, Marc , Chen, Baoquan . Visual Localization via Few-Shot Scene Region Classification . (2022) : 393-402 . |
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