An incremental semi-supervised approach for visual domain adaptation

The general focus of domain adaptation methodology is transferring learned knowledge from labeled train domain to unlabeled test domain. Domain adaptation tries to minimize the domain shift problem by modeling a classifier using labeled training domain data which taken under definite conditions and this classifier is utilize to test the data which taken under distinct conditions. Common adaptation approaches will learn a freshly acquired feature vector space using labeled data domain (source) and unlabeled train (target) data domain having alike characteristics and a supervised, unsupervised or semi-supervised classifier will carry out the further task. Here is a design of an incremental KM-ELM classifier which can utilize for better classification of various domain adaptation task. This classifier is a fusion of high performing K-Means algorithm and fast neural network Extreme learning machine (ELM). Here utilizes the cross-domain learning capability of ELM with PCA, GFK (Geodesic flow Kernel) methods for addressing domain adaptation task. First PCA and PLS are used to create the subspaces of testing data and training data and these subspaces will considered as a points in Grassmann manifold. After that the geodesic based domain shift representation will carry out and integration of these data points creates the intermediate cross domain. This will form a new space having feature vectors from training domain and testing domain where the likelihood of these vectors in this space is maximum.