In this paper, a nonparametric discriminant multi-manifold learning method is proposed for dimensionality reduction. In the proposed method, manifolds distance ...
In this paper, a nonparametric discirminant multi-manifold learning (NDML) method is presented for dimensionality reduction. Based on the assumption that ...
Nonparametric discriminant multi-manifold learning for ...
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Nonparametric discriminant multi-manifold learning (NDML) [37] adopts LLE to preserve local geometry, and models separabilities between classes by manifold ...
Abstract. In this paper, a nonparametric discirminant multi-manifold learning. (NDML) method is presented for dimensionality reduction. Based on the.
Experiments validate that NDML is of better performance than some other dimensionality reduction methods, such as Unsupervised Discriminant Projection (UDP) ...
In this paper, a nonparametric discirminant multi-manifold learning (NDML) method is presented for dimensionality reduction. Based on the assumption.
Manifold learning is an approach to non-linear dimensionality reduction. Algorithms for this task are based on the idea that the dimensionality of many data ...
Abstract—Positive and Unlabeled (PU) learning has attracted intensive research interests in recent years, which is capable of training a binary classifier ...
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In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for an image feature extraction and pattern recognition based on graph ...
Linear discriminant analysis (LDA) is one representative approach to learning discriminant ... These methods achieved interesting results of multi-manifold ...