Semi-supervised rough fuzzy Laplacian Eigenmaps for dimensionality reduction

Ma, Minghua; Deng, Tingquan; Wang, Ning; Chen, Yanmei

doi:10.1007/s13042-018-0784-y

Cited by 14 publications

(7 citation statements)

References 49 publications

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“…This method is based on manifold regularization utilizing unlabeled data in the training step. Ma et al proposed another semi-supervised nonlinear feature extraction based on Laplacian eigenmap, where its initial graph is obtained using fuzzy similarity relation [42]. A semi-supervised dimensionality reduction combined with feature weighting is also proposed in [43].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The focus of this paper is on the representation of opinion data using a nonlinear feature extraction method by considerying laying data on one manifold and discoverying this structure by sparse properties, which have neglicated in the literature. Similar to existing nonlinear methods [41], a graph is constructed from data, but instead of using 𝑘-nearest neighbor (𝑘NN) or 𝜖-nearest neighbor with some heuristics [41,42], a graph is learned from data in this paper in order to detect close data points by imposing sparsity constraint.…”

Section: Literature Reviewmentioning

confidence: 99%

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Manifold-based Sparse Representation for Opinion Mining

Karimi

2023

Preprint

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This paper focuses on how to enhance feature representation for opinion mining. The classical feature representation methods suffer from high dimensionality, sparsity , noisy, irrelevant and redundant information. It is proposed to exploit the manifold assumption and sparse property as prior knowledge for opinion representation to learn effective features. First, the graph representation of user reviews based on the mentioned prior knowledge is learned. Then, the spectral properties of the learned graph are exploited to present data in a new feature space. The proposed algorithm is applied to four various common input features on two benchmark datasets, Internet Movie Database (IMDB) and Amazon review dataset. Our experiments reveal that the proposed algorithm yields considerable enhancements in terms of F-measure, accuracy, and other standard performance measures compared to the combination of state-of-the-art features with various classifiers. The highest classification accuracies of 99.15 and 91.97 are obtained in the proposed method on IMDB and Amazon exploiting linear SVM classifier, respectively. The impact of parameters of the proposed algorithm is also investigated in this paper.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Manifold-based Sparse Representation for Opinion Mining

Karimi

2023

Preprint

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“…A k-nearest neighborhood graph or an ε-ball neighborhood graph is constructed and weights of edges (between vertices) are assigned using the Gaussian kernel function or 0-1 weighting method [31]. Given a dataset X = {x 1 , x 2 , ..., x n } with n samples, each sample x i ∈ X has m features.…”

Section: Laplacian Eigenmapsmentioning

confidence: 99%

Deep Fully Convolutional Embedding Networks for Hyperspectral Images Dimensionality Reduction

Zhou

Shi

et al. 2021

Remote Sensing

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Due to the superior spatial–spectral extraction capability of the convolutional neural network (CNN), CNN shows great potential in dimensionality reduction (DR) of hyperspectral images (HSIs). However, most CNN-based methods are supervised while the class labels of HSIs are limited and difficult to obtain. While a few unsupervised CNN-based methods have been proposed recently, they always focus on data reconstruction and are lacking in the exploration of discriminability which is usually the primary goal of DR. To address these issues, we propose a deep fully convolutional embedding network (DFCEN), which not only considers data reconstruction but also introduces the specific learning task of enhancing feature discriminability. DFCEN has an end-to-end symmetric network structure that is the key for unsupervised learning. Moreover, a novel objective function containing two terms—the reconstruction term and the embedding term of a specific task—is established to supervise the learning of DFCEN towards improving the completeness and discriminability of low-dimensional data. In particular, the specific task is designed to explore and preserve relationships among samples in HSIs. Besides, due to the limited training samples, inherent complexity and the presence of noise in HSIs, a preprocessing where a few noise spectral bands are removed is adopted to improve the effectiveness of unsupervised DFCEN. Experimental results on three well-known hyperspectral datasets and two classifiers illustrate that the low dimensional features of DFCEN are highly separable and DFCEN has promising classification performance compared with other DR methods.

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“…A semi-supervised rough fuzzy Laplacian Eigenmaps (SSRFLE) approach was presented by Minghua et al [42]. Their model works to construct a set of semi-supervised fuzzy similarity granules to assess the similarity between samples.…”

Section: B Heart Disease Prediction Techniquesmentioning

confidence: 99%

Novel Feature Reduction (NFR) Model With Machine Learning and Data Mining Algorithms for Effective Disease Risk Prediction

Pasha

Mohamed

2020

IEEE Access

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Presently, the application of machine learning (ML) and data mining (DM) techniques have a vital role in healthcare systems and wisely convert all obtainable data into beneficial knowledge. It is proven from the literature works that a chance of 12% error remains in the diagnosis of the diseases by the medical practitioners. Moreover, for effective disease risk prediction in medical analysis, more emphasis is accorded to the area under the curve (AUC) with accuracy as an evaluation metric. However, the role of the AUC has not been previously characterized notably. In this research article, a novel feature reduction (NFR) model that is aligned with the ML and DM algorithms is proposed to reduce the error rate and further improve the performance. The proposed NFR model comprises of two approaches and uses the AUC in addition to the accuracy to achieve a robust and effective disease risk prediction. The first approach is based on a heuristic process evaluating performance by reducing features with respect to the improvement in the AUC besides the accuracy as evaluation metrics, working to obtain the best subset of highly contributing features in the prediction. The second approach evaluates the accuracy and AUC of all individual features and forms the subsets with the highest accuracies, AUCs, and least difference between them, which are combined in various combinations to achieve the best-reduced set of highly relevant features. For this purpose, the benchmarked public heart datasets of the ML repository of the University of California, Irvine (UCI) are tested; the results are promising. The highest accuracy and AUC achieved with the proposed NFR model are 95.52% and 99.20% with 41.67% feature reduction, respectively. The accuracy is 4.22% higher than recent existing research with a significant improvement of 25% in the performance of the running time of the algorithm.

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Semi-supervised rough fuzzy Laplacian Eigenmaps for dimensionality reduction

Cited by 14 publications

References 49 publications

Manifold-based Sparse Representation for Opinion Mining

Manifold-based Sparse Representation for Opinion Mining

Deep Fully Convolutional Embedding Networks for Hyperspectral Images Dimensionality Reduction

Novel Feature Reduction (NFR) Model With Machine Learning and Data Mining Algorithms for Effective Disease Risk Prediction

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