Novel unsupervised anomaly detection based on robust principal component classifier;

The paper proposes an unsupervised training method for acquiring probability models that accurately segment Chinese character sequences into words.

In order to overcome the weakness of statistical texture segmentation method,a new unsupervised texture segment algorithm,based on multi-resolution statistical model,was present.

To avoid the disadvantage of getting into local optimum solution with general numerical computation methods in the general independent component analysis and the restriction of neuron activation functions of neural learning algorithm,an improved model of independent component analysis(ICA) based on genetic algorithm was proposed for the unsupervised classification of hyperspectral data.

In order to classify the data of Hyperspectral remote sensing images automatically without prior knowledge,an unsupervised classification algorithm is presented based on the conception of convex geometry and spectral features in this paper.

In this study,a novel artificial immune system algorithm for unsupervised classification and recognition is proposed by using a novel manifold distance based dissimilarity measure which can measure the geodesic distance along the manifold.

The learning of connectionism,which consists mainly of supervised learning,intensive learning and unsupervised learning,is modelled after the learning of human beings.

The result of the feature selection in unsupervised learning is not as satisfactory as that in supervised learning.

The paper puts forward the method that based on the neural network unsupervised learning, also, improves the index on separation effects.

Multiresolution likelihood ratio for unsupervised segmentation of SAR imagery;

An unsupervised segmentation of SAR imagery based on Multiscale image block is proposed.