A novel unsupervised Globality-Locality Preserving Projections in transfer learning

Sanodiya, Rakesh Kumar; Mathew, Jimson

doi:10.1016/j.imavis.2019.08.006

Cited by 10 publications

(3 citation statements)

References 24 publications

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“…As an extension of TCA, Joint Distribution Adaptation (JDA) [14] aligned conditional and marginal distribution simultaneously using pseudo labels, then an iterative training scheme is employed to obtain more accurate labels. Inspired by this, a series of studies tried to modify classical feature extraction methods (e.g., linear discriminant analysis and locality preserving projections [15,16]) by means of pseudo labels.…”

Section: Pseudo Label Without Selectionmentioning

confidence: 99%

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Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation

Min

Zhou

et al. 2021

Applied Sciences

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Distribution mismatch can be easily found in multi-sensor systems, which may be caused by different shoot angles, weather conditions and so on. Domain adaptation aims to build robust classifiers using the knowledge from a well-labeled source domain, while applied on a related but different target domain. Pseudo labeling is a prevalent technique for class-wise distribution alignment. Therefore, numerous efforts have been spent on alleviating the issue of mislabeling. In this paper, unlike existing selective hard labeling works, we propose a fuzzy labeling based graph learning framework for matching conditional distribution. Specifically, we construct the cross-domain affinity graph by considering the fuzzy label matrix of target samples. In order to solve the problem of representation shrinkage, the paradigm of sparse filtering is introduced. Finally, a unified optimization method based on gradient descent is proposed. Extensive experiments show that our method achieves comparable or superior performance when compared to state-of-the-art works.

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Section: Pseudo Label Without Selectionmentioning

confidence: 99%

“…Performance (accuracy %) on Office-Caltech10 (No [1][2][3][4][5][6][7][8][9][10][11][12]. and ImageCLEF (No [13][14][15][16][17][18]…”

mentioning

confidence: 99%

Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation

Min

Zhou

et al. 2021

Applied Sciences

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“…Moreover, it was impossible to collect enough meaningful data required to train a model, although many researchers attempted to gather them. However, various transfer learning methods have been proposed for transferring knowledge in the context of features, instant weights, parameters, or relationship information between data samples in a domain [13][14][15][16]. Figure 1 shows four steps of creating a complete model using transfer learning.…”

Section: Transfer Learningmentioning

confidence: 99%

The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning

Choe

Choi

Kim

2020

Mobile Information Systems

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Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area.

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Dimension Reduction of Multidimensional Structured and Unstructured Datasets through Ensemble Learning of Neural Embeddings

Alvarado‐Pérez,

Garcia,

Puig

2024

Advanced Intelligent Systems

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Dimension reduction aims to project a high‐dimensional dataset into a low‐dimensional space. It tries to preserve the topological relationships among the original data points and/or induce clusters. NetDRm, an online dimensionality reduction method based on neural ensemble learning that integrates different dimension reduction methods in a synergistic way, is introduced. NetDRm is designed for datasets of multidimensional points that can be either structured (e.g., images) or unstructured (e.g., point clouds, tabular data). It starts by training a collection of deep residual encoders that learn the embeddings induced by multiple dimension reduction methods applied to the input dataset. Subsequently, a dense neural network integrates the generated encoders by emphasizing topological preservation or cluster induction. Experiments conducted on widely used multidimensional datasets (point‐cloud manifolds, image datasets, tabular record datasets) show that the proposed method yields better results in terms of topological preservation ( curves), cluster induction (V measure), and classification accuracy than the most relevant dimension reduction methods.

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A novel unsupervised Globality-Locality Preserving Projections in transfer learning

Cited by 10 publications

References 24 publications

Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation

Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation

The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning

Dimension Reduction of Multidimensional Structured and Unstructured Datasets through Ensemble Learning of Neural Embeddings

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