Harnessing the discriminatory strength of dictionaries

Over the past few years there are many developments in the area of classification in data mining. Classification is a supervised learning method, that maps data into predefined groups or classes. Nowadays classification techniques are extensively used in different applications. In this area most of the research works are done on text, image, signal etc. The main goal of this paper is to use a dictionary-based approach to learn, represent and classify documents. We consider dictionary as a collection of documents and each document in the dictionary is represented as a collection of vectors. An algorithm is also implemented to easily locate a class specific document in the dictionary and if it is not present, update the dictionary. The existing method is based on a dictionary learning algorithm which only improves the document representation based on Singular Value Decomposition (SVD) updation. Since SVD will not be helpful for discrimination of data, so our proposed algorithm is Linear Discriminant Analysis (LDA) for learning a discriminating dictionary. On applying the proposed algorithm on well known dataset, the overall results obtained shows 90% improvement in accuracy. The advantage is that it can be used for both representation as well as classification.