1,721,262 research outputs found
Multilabel distribution learning based on multi-output regression and manifold learning
No full textReal-world multilabel data are high dimensional, and directly using them for label distribution learning (LDL) will incur extensive computational costs. We propose a multilabel distribution learning algorithm based on multioutput regression through manifold learning, referred to as MDLRML. By exploiting smooth, similar spaces' information provided by the samples' manifold learning and LDL, we link the two spaces' manifolds. This facilitates using the topological relationship of the manifolds in the feature space to guide the manifold construction of the label space. The smoothest regression function is used to fit the manifold data, and a locally constrained multioutput regression is designed to improve the data's local fitting. Based on the regression results, we enhance the logical labels into the label distributions, thereby mining and revealing the label's hidden information regarding importance or significance. Extensive experimental results using real-world multilabel datasets show that the proposed MDLRML algorithm significantly improves the multilabel distribution learning accuracy and efficiency over several existing state-of-the-art schemes.</p
A novel probabilistic label enhancement algorithm for multi-label distribution learning
No full textWe propose a novel probabilistic label enhancement algorithm, called PLEA, to solve challenging label distribution learning (LDL) for multi-label classification problems. We adopt the well-known maximum entropy model based label distribution learner. However, unlike the existing LDL algorithms based on the maximum entropy model, we propose to use manifold learning to enhance the label distribution learner. Specifically, the supervised information in the label manifold is utilized in the feature manifold space construction to improve the accuracy of feature extraction, while dramatically reducing the feature dimension. Then the robust linear regression is employed to estimate the label distributions associated with the extracted reduced-dimension features. Using the enhanced reduced-dimension features and their associated estimated label distributions in the maximum entropy model, the unknown true label distributions can be estimated more accurately, while imposing considerably lower computational complexity. We evaluate the proposed PLEA method on a wide-range artificial and high-dimensional real-world datasets. Experimental results obtained demonstrate that our proposed PLEA method has advantages in LDL accuracy and runtime performance, compared to the latest multi-label LDL approaches. The results also show that our PLEA compares favourably with the state-of-the-arts multi-label learning algorithms for classification tasks
A label distribution manifold learning algorithm
No full textIn this paper, we propose a novel label distribution manifold learning (LDML) method for solving the multilabel distribution learning problem. First, using manifold learning, we extract the accurate and reduced-dimension features of the training data. Second, we estimate the unknown label distributions associated with the extracted reduced-dimension features based on multi-output kernel regression. Third, we use the extracted reduced-dimension features and their associated estimated label distributions to form an enhanced maximum entropy model, which enables us to accurately and efficiently estimate the unknown true label distributions for the training data. We refer to this algorithm as the LDML. We also propose to apply the tangent space alignment regression in the second stage, and the resulting algorithm is called the LDML-R. The LDML-R has better label distribution learning performance than the LDML but imposes higher complexity than the latter. We evaluate the proposed LDML and LDML-R algorithms on 15 real-world data sets with ground-truth label distributions, and the experimental results obtained show that our method has advantages in terms of learning accuracy compared to the latest multi-label distribution learning approaches. We also use another 10 real-world multi-class data sets, which do not have the ground-truth label distributions, to demonstrate the superior multilabel classification performance of our LDML-R algorithm over the existing state-of-the-art multi-label classification algorithms
A novel label enhancement algorithm based on manifold learning
No full textWe propose a label enhancement model to solve the multi-label learning (MLL) problem by using the incremental subspace learning to enrich the label space and to improve the ability of label recognition. In particular, we use the incremental estimation of the feature function representing the manifold structure to guide the construction of the label space and to transform the local topology from the feature space to the label space. First, we build a recursive form for incremental estimation of the feature function representing the feature space information. Second, the label propagation is used to obtain the hidden supervisory information of labels in the data. Finally, an enhanced maximum entropy model based on conditional random field is established as the objective, to obtain the predicted label distribution. The enriched label information in the manifold space obtained in first step and the estimated label distributions provided in second step are employed to train this enhanced maximum entropy model by a gradient-descent iterative optimization to obtain the label distribution predictor's parameters with enhanced accuracy. We evaluate our method on 24 real-world datasets. Experimental results demonstrate that our label enhancement manifold learning model has advantages in predictive performance over the latest MLL methods.<br/
Label enhancement via manifold approximation and projection with graph convolutional network
No full textLabel enhancement (LE) aims to enrich logical labels into their corresponding label distributions. But existing LE algorithms fail to fully leverage the structural information in the feature space to improve LE learning. To address this key issue, we first apply manifold learning to map the relatedness between low-dimensional feature samples to the label space. Based on the smoothness assumption of manifolds, the implicit correlation between low-dimensional feature and label spaces effectively promotes the LE process, enabling the learning model to accurately capture the mapping relationship between feature and label manifolds. This leads to an LE based on feature representation (LEFR) algorithm. We also propose an LE algorithm based on graph convolutional network (GCN), called LE-GCN. Inspired by the relationship between threshold connections and label connections, we extend GCN to the LE field for the first time to fully exploit the hidden relationships between nodes and labels. By enhancing node information with threshold connections and label connections, the label learning accuracy reaches a new level. Experiments on real-world datasets show that our LEFR and LE-GCN outperform several state-of-the-art LE algorithms
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
RG4LDL: renormalization group for label distribution learning
No full textLabel distribution learning (LDL) is an effective paradigm to address label ambiguity by modeling the relevance of multiple labels to an instance. However, existing LDL methods suffer from challenges such as high model complexity, slow convergence, and limited availability of label distribution annotated training data. To tackle these issues,we propose RG4LDL, a novel framework that integrates the renormalization group (RG) principle with LDL for the first time. RG4LDL employs a restricted Boltzmann machine (RBM)-based neural network to iteratively extract relevant degrees of freedom, thereby optimizing feature learning and improving predictive accuracy. By combining unsupervised RG learning and supervised LDL prediction in an end-to-end manner, RG4LDL achieves both efficiency and effectiveness. Experimental results on 13 real-world datasets and a synthetic toy dataset demonstrate that RG4LDL significantly outperforms state-of-the-art LDL methods in terms of predictive accuracy and computational efficiency. These results highlight the potential of RG4LDL as a benchmark solution for label distribution learning tasks
Effects of mechanical dispersion on the morphological evolution of a chemical dissolution front in a fluid-saturated porous medium
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