Adaptive neighborhood selection for manifold learning

Wei, Jia; Peng, Hong; Lin, Yi-Shen; Huang, Zhimao; Wang, Jiabing

doi:10.1109/icmlc.2008.4620435

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Thus, in order to give full play to the abilities of NPE, an adaptive neighborhood algorithm is taken. The algorithm can select the optimal neighbor parameter k only depending on the input data, which is based on estimates of intrinsic dimensionality and tangent orientation [43].…”

Section: ) Npe Convolutionmentioning

confidence: 99%

Global and Local Structure Network for Image Classification

2023

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Principal Component Analysis Network (PCANet) is a feature learning algorithm which is widely used in face recognition and object classification. However, original PCANet still has some shortages. One is that PCA algorithm only extracts features by considering the global structure. The other lies in that the original PCANet only employs one particular single layer convolutional results, which loses the information of other convolutional layers. In this paper, we propose a new simple and efficient convolutional neural network called global and local structure network (GLSNet) to address the problems. The network extracts the features both from the global structure and the local structure of the original data space. Specifically, a principal component analysis (PCA) convolutional layer which learns the filters by PCA algorithm is used to remove the noises and redundant information at the first stage. Then at the second stage, another PCA convolution is added to extract features by considering the global structure. As for the local structure, we use the neighborhood preserving embedding (NPE) algorithm to learn the convolutional filters. At the output stage, the global structure feature extracted by PCA convolution and the local structure feature extracted by NPE convolution is concatenated as a united feature. Furthermore, the first layer convolutional feature is also taken into consideration to obtain shallow-level information. Finally, these features are concatenated as a united feature, and a spatial pyramid pooling layer is followed to pool above the united features. To test the effectiveness of the proposed algorithm, the experiments on some image datasets, including three types: human face dataset, object dataset, and handprinted dataset, proceeded. And it performs better than the original PCANet and some improvement algorithms of PCANet, such as PLDANet, and MMPCANet.

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Section: ) Npe Convolutionmentioning

confidence: 99%

Global and Local Structure Network for Image Classification

2023

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“…To address the above issues, we introduce a complementary spectrum as a supplement to the experimental spectrum, which has been proven useful in database search methods [8] . MS2 data can be represented as a set of peaks {(𝑚 !…”

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confidence: 99%

Introducingπ-HelixNovo for practical large-scale de novo peptide sequencing

Yang

Ling

Sun

et al. 2023

Preprint

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De novo peptide sequencing is a promising approach for novel peptide discovery. We use a novel concept of complementary spectra to enhance ion information and propose a de novo sequencing model PandaNovo based on Transformer architecture. PandaNovo outperforms other state-of-the-art models and enhances the taxonomic resolution of gut metaproteome, taking a significant step forward in de novo sequencing.

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Machine Learning for Flow Battery Systems

A. Shah,

Leung,

et al. 2023

Engineering Applications of Computational Methods

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Adaptive neighborhood selection for manifold learning

Cited by 6 publications

References 10 publications

Global and Local Structure Network for Image Classification

Global and Local Structure Network for Image Classification

Introducingπ-HelixNovo for practical large-scale de novo peptide sequencing

Machine Learning for Flow Battery Systems

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