Video and image datasets can often be described by a small number of parameters, even though each image usually consists of hundreds or thousands of pixels. This observation is of...
Embedding images into a low dimensional space has a wide range of applications: visualization, clustering, and pre-processing for supervised learning. Traditional dimension reduct...
In this paper we present a new algorithm to transform an RGB color image to a grayscale image. We propose using non-linear dimension reduction techniques to map higher dimensional ...
Ming Cui, Jiuxiang Hu, Anshuman Razdan, Peter Wonk...
Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dimensionality reduction, Linear Discriminant Analysis (LDA) is one of the most po...
Feiping Nie, Shiming Xiang, Yangqiu Song, Changshu...