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In this work, sub-manifold projections based semi-supervised dimensionality reduction (DR) problem learning from partial constrained data is discussed. Two semi-supervised DR algorithms termed Marginal Semi-Supervised Sub-Manifold Projections (MS³MP) and orthogonal MS³MP (OMS³MP) are proposed. MS³MP in the singular case is also discussed. We also present(More)
This paper presents a two-dimensional Neighborhood Preserving Projection (2DNPP) for appearance-based face representation and recognition. 2DNPP enables us to directly use a feature input of 2D image matrices rather than 1D vectors. We use the same neighborhood weighting procedure that is involved in NPP to form the nearest neighbor affinity graph.(More)
Linear Discriminant Analysis (LDA) is a well-known dimensionality reduction algorithm for pattern recognition and machine learning. And Trace Ratio LDA (TR-LDA) and Null-space LDA (NLDA) are two popular variants of LDA. Both NLDA and TR-LDA can result in orthogonal transformations. However, they applied different schemes in deriving the optimal(More)
Most non-small cell lung cancer (NSCLC) patients responding to gefitinib harbor activating mutations in the epidermal growth factor receptor (EGFR). However, the responsive cases eventually develop the resistance to gefitinib. Besides, K-ras mutations were identified as the primary resistance to gefitinib. We investigated whether Marsdenia tenacissima(More)
Caesalpinia sappan L. (Lignum Sappan) is a Chinese medicinal plant for treating ischemic cerebral apoplexy. Deoxysappanone B (DSB), a homoisoflavone compound isolated from C. sappan L. (Lignum Sappan), was studied for anti-neuroinflammatory and neuroprotective properties using lipopolysaccharide (LPS)-induced BV-2 microglia neuroinflammation model and(More)
A sensitive HPLC-DAD-ESI-MS/MS method was established to screen and identify the polymethoxylated flavonoids (PMFs) in the leaves of Murraya paniculata (L.) Jack. 16 PMF standards were first to be analyzed in positive mode by the CID-MS/MS. For polymethoxylated flavones, the fragments of [M+H-n×15](+) produced by loss of one or more methyl radicals from the(More)
Visualizing similarity data of different objects by exhibiting more separate organizations with local and multimodal characteristics preserved is important in multivariate data analysis. Laplacian Eigenmaps (LAE) and Locally Linear Embedding (LLE) aim at preserving the embeddings of all similarity pairs in the close vicinity of the reduced output space, but(More)
In this paper, we propose a semisupervised label consistent dictionary learning (SSDL) framework for machine fault classification. SSDL is a semisupervised extension of recent fully supervised label consistent dictionary learning approach, since the number of labeled machine data is usually limited in practice. To enable the supervised dictionary learning(More)
—Bearings are critical components in induction motors and brushless direct current motors. Bearing failure is the most common failure mode in these motors. By implementing health monitoring and fault diagnosis of bearings, unscheduled maintenance and economic losses caused by bearing failures can be avoided. This paper introduces trace ratio linear(More)
Isomap is a well-known nonlinear dimensionality reduction (DR) method, aiming at preserving geodesic distances of all similarity pairs for delivering highly nonlinear manifolds. Isomap is efficient in visualizing synthetic data sets, but it usually delivers unsatisfactory results in benchmark cases. This paper incorporates the pairwise constraints into(More)