Classification by semi-supervised discriminative regularization

TL;DR: Though the proposed method exploits only one training sample per class to perform classification, it might obtain a better performance than the nearest feature space method proposed in Chien and Wu, which depends on all the training samples to classify the test sample.

TL;DR: Based on the MaSF-based EBRB, the k-neighbor search and the best activated rule set algorithms are further proposed to find both the unique and the desired rules for each decision-making process without visiting the entire EbrB, especially when handling classification problems with large attribute datasets.

TL;DR: A least squares version of Lap-TSVM is formulated, termed as Lap-LSTSVM, leading to an extremely fast approach for generating semi-supervised classifiers, and an efficient conjugate gradient algorithm is further developed for solving the systems of linear equations.

TL;DR: A semi-supervised distance metric learning method by exploring feature correlations using unlabeled samples to calculate the prediction error by means of local linear regression and fuse the knowledge learned from both labeled and unlabeling samples into an overall objective function which can be solved by maximum eigenvectors.

TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.

TL;DR: An approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set and efficiently computes a globally optimal solution, and is guaranteed to converge asymptotically to the true structure.

TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.