Semi-supervised locally discriminant projection for classification and recognition
S Zhang, YK Lei, YH Wu - Knowledge-Based Systems, 2011 - Elsevier
Semi-supervised dimensional reduction methods play an important role in pattern
recognition, which are likely to be more suitable for plant leaf and palmprint classification …
recognition, which are likely to be more suitable for plant leaf and palmprint classification …
Dimension reduction using semi-supervised locally linear embedding for plant leaf classification
S Zhang, KW Chau - … and Applications: 5th International Conference on …, 2009 - Springer
Plant has plenty use in foodstuff, medicine and industry, and is also vitally important for
environmental protection. So, it is important and urgent to recognize and classify plant …
environmental protection. So, it is important and urgent to recognize and classify plant …
A unified framework for semi-supervised dimensionality reduction
In practice, many applications require a dimensionality reduction method to deal with the
partially labeled problem. In this paper, we propose a semi-supervised dimensionality …
partially labeled problem. In this paper, we propose a semi-supervised dimensionality …
Semi-supervised dimensionality reduction using estimated class membership probabilities
W Li, Q Ruan, J Wan - Journal of Electronic Imaging, 2012 - spiedigitallibrary.org
In solving pattern-recognition tasks with partially labeled training data, the semi-supervised
dimensionality reduction method, which considers both labeled and unlabeled data, is …
dimensionality reduction method, which considers both labeled and unlabeled data, is …
Learning from normalized local and global discriminative information for semi-supervised regression and dimensionality reduction
Semi-supervised dimensionality reduction is one of the important topics in pattern
recognition and machine learning. During the past decade, Laplacian Regularized Least …
recognition and machine learning. During the past decade, Laplacian Regularized Least …
Image classification via least square semi-supervised discriminant analysis with flexible kernel regression for out-of-sample extension
Semi-supervised dimensionality reduction is an important research topic in many pattern
recognition and machine learning applications. Among all the methods for semi-supervised …
recognition and machine learning applications. Among all the methods for semi-supervised …
Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction
Dealing with high-dimensional data has always been a major problem in many pattern
recognition and machine learning applications. Trace ratio criterion is a criterion that can be …
recognition and machine learning applications. Trace ratio criterion is a criterion that can be …
Locality preserving and global discriminant projection with prior information
Existing supervised and semi-supervised dimensionality reduction methods utilize training
data only with class labels being associated to the data samples for classification. In this …
data only with class labels being associated to the data samples for classification. In this …
Supervised locality pursuit embedding for pattern classification
In pattern recognition research, dimensionality reduction techniques are widely used since it
may be difficult to recognize multidimensional data especially if the number of samples in a …
may be difficult to recognize multidimensional data especially if the number of samples in a …
Dimensionality reduction using graph-embedded probability-based semi-supervised discriminant analysis
W Li, Q Ruan, J Wan - Neurocomputing, 2014 - Elsevier
Probabilistic semi-supervised discriminant analysis (PSDA) is a recently proposed semi-
supervised dimensionality reduction approach. It quantifies class membership probability to …
supervised dimensionality reduction approach. It quantifies class membership probability to …