In this work, we show the importance of multidimensional opinion representation in the political context combining domain knowledge and results from principal component analysis. ...
Clustering data in high dimensions is believed to be a hard problem in general. A number of efficient clustering algorithms developed in recent years address this problem by proje...
Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu,...
In this paper, we focus on the use of context-aware, collaborative filtering, machine-learning techniques that leverage automatically sensed and inferred contextual metadata toget...
Marc Davis, Michael Smith, John F. Canny, Nathan G...
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...
In wireless sensor networks, energy consumption is generally associated with the amount of sent data once communication is the activity of the network that consumes more energy. T...