Small sample learning with high order contractive auto-encoders and application in SAR images

Yang, Qianwen; Sun, Fuchun

doi:10.1007/s11432-017-9214-8

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…The trained models that learned feature representations using CAE are highly robust to minor noises and small changes within the training data. CAEs are considered the extended forms of the DAEs in which contractive penalty is added to the error function of reconstruction [25]. This penalty is, in turn, used to penalize the attribute sensitivity to the variations in the inputs.…”

Section: Contractive Auto-decodermentioning

confidence: 99%

Auto-Encoder Variants for Solving Handwritten Digits Classification Problem

Aamir¹,

Nawi²,

Mahdin³

et al. 2020

IJFIS

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Auto-encoders (AEs) have been proposed for solving many problems in the domain of machine learning and deep learning since the last few decades. Due to their satisfactory performance, their multiple variations have also recently appeared. First, we introduce the conventional AE model and its different variant for learning abstract features from data by using a contrastive divergence algorithm. Second, we present the major differences among the following three popular AE variants: sparse AE (SAE), denoising AE (DAE), and contractive AE (CAE). Third, the main contribution of this study is performing the comparative study of the aforementioned three AE variants on the basis of their mathematical modeling and experiments. All the variants of the standard AE are evaluated on the basis of the MNIST benchmark handwritten digit dataset for classification problem. The observed output reveals the benefit of using the AE model and its variants. From the experiments, it is concluded that CAE achieved better classification accuracy than those of SAE and DAE.

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Section: Contractive Auto-decodermentioning

confidence: 99%

Auto-Encoder Variants for Solving Handwritten Digits Classification Problem

Aamir¹,

Nawi²,

Mahdin³

et al. 2020

IJFIS

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“…Thus, we can train the protein-specific deep learning models for the homologous RBPs with a large number of verified binding sites, and then transfer these trained models to predict binding targets for this RBP. Another strategy may be using meta learning, also related to small sample learning (Yang & Sun, 2018), which learns more transferable representations of data than others, thus the learned representations can be transferred to other RBPs with high accuracy (Schaul & Schmidhuber, 2010).…”

Section: Homology-based Transfer Learning For Poorly Studied Rbpsmentioning

confidence: 99%

Recent methodology progress of deep learning for RNA–protein interaction prediction

et al. 2019

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Interactions between RNAs and proteins play essential roles in many important biological processes. Benefitting from the advances of next generation sequencing technologies, hundreds of RNA-binding proteins (RBP) and their associated RNAs have been revealed, which enables the large-scale prediction of RNA-protein interactions using machine learning methods. Till now, a wide range of computational tools and pipelines have been developed, including deep learning models, which have achieved remarkable performance on the identification of RNA-protein binding affinities and sites. In this review, we provide an overview of the successful implementation of various deep learning approaches for predicting RNA-protein interactions, mainly focusing on the prediction of RNA-protein interaction pairs and RBP-binding sites on RNAs. Furthermore, we discuss the advantages and disadvantages of these approaches, and highlight future perspectives on how to design better deep learning models. Finally, we suggest some promising future directions of computational tasks in the study of RNA-protein interactions, especially the interactions between noncoding RNAs and proteins. deep learning, feature representation, machine learning, motif discovery, RNA-protein interactions *The first two authors are co-first authors.

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“…The improved autoencoder with different regularization constraints, such as sparse autoencoder has better performance in various target recognition studies [21] 。 Therefore, we adopt the autoencoder method to achieve dimensionality reduction and find the low-dimensional angle invariance features of high-dimensional HRRP data. In [22], manifold learning method is combined with antoencoder, but the goal of this paper is to improve the performance of autoencoder. Our goal of combining the two methods is to form a novel framework for the extraction and analysis of target angle-related features and component correlation.…”

Section: Introductionmentioning

confidence: 99%

Sparse Autoencoder Based Manifold Analyzer Model of Multi-Angle Target Feature

Chen¹,

Peng²,

Li³

et al. 2020

IEEE Access

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Automatic target recognition (ATR) has always been an important research topic, and the performance is affected by feature extraction. High-resolution range profiles (HRRP) contains structural information of target from different angles. Existing target recognition algorithms are mostly adopt the supervised learning and verify the validity through classification accuracy. However, these methods cannot satisfy the information acquisition such as correlation, composition analysis and attitude analysis of unlabeled or uncoordinated targets. Therefore, we are committed to researching new intelligent feature extraction and analysis method. In this paper, a novel learning framework is proposed to realize the angle correlation feature extraction and feature analysis of multi-angle HRRP targets. A multi-layer sparse autoencoder is applied to extract HRRP features and the extracted features are mapped to the lowdimensional space by manifold learning method to find the correlation distribution of HRRP data. The effectiveness of the proposed framework is demonstrated by experiments with simulated and measured data. The experimental results show that the framework realizes the extraction of angle-invariant features, and the analysis of distribution relationship between HRRP data and angles. The research results of the correspondence between the assembly and the related geometry provide the basis and possibility for further identification and composition determination.

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Small sample learning with high order contractive auto-encoders and application in SAR images

Cited by 3 publications

References 7 publications

Auto-Encoder Variants for Solving Handwritten Digits Classification Problem

Auto-Encoder Variants for Solving Handwritten Digits Classification Problem

Recent methodology progress of deep learning for RNA–protein interaction prediction

Sparse Autoencoder Based Manifold Analyzer Model of Multi-Angle Target Feature

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