Activity Recognition for Traditional Dances Using Dimensionality Reduction

Activity recognition is a complex problem mainly because of the nature of the data. Data usually are high dimensional, so applying a classifier directly to the data is not always a good practice. A common method is to find a meaningful representation of complex data through dimensionality reduction. In this paper we propose novel kernel matrices based on graph theory to be used for dimensionality reduction. The proposed kernel can be embedded in a general dimensionality reduction framework. Experiments on a traditional dance recognition dataset are conducted and the advantage of using dimensionality reduction before classification is highlighted.