All these algorithms considered the problem of classification, either transductive or inductive. In this paper, we aim at dimensionality reduction in the semi- ...
In this paper, we propose a dimensionality reduction method called semi-supervised. TransductIve Discriminant Analysis (TIDA) which preserves the global and ...
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High-dimensional data requires scalable algorithms. We propose and analyze four scalable and related algorithms for semi-supervised discriminant analysis ...
In this section, we present our semi-supervised discriminant analysis algorithm in three phases: spectral transduction via constrained Normalized Cuts, labeled ...
In this paper, we propose a novel dimensional- ity reduction method, called Semi-Supervised Discriminant. Analysis (SSDA), which can utilize both labeled and ...
Based on manifold assumption. Most existing methods are transductive, with very few exceptions,. e.g., Laplacian SVM (LapSVM) [Belkin et al., 2005] ...
In this paper, we propose a semi-supervised robust discriminative classification method based on the least-squares formulation of linear discriminant analysis ...
We propose a novel semi-supervised discriminant analysis algorithm called SSDACCCP . We utilize unlabeled data to maximize an optimality criterion of LDA and ...
We propose a new semi-supervised self-training approach which is used to automatically augment the manually labeled training set with new unlabeled data. Semi- ...
This paper proposes a novel method, called Semi- supervised Discriminant Analysis (SDA), which makes use of both labeled and unlabeled samples to learn a ...