Unsupervised Spectral Regression Learning for Pyramid HOG (Journal of Fiber Bioengineering and Informatics, 2015)
Applying the original raw data to machine learning will bring in a poor performance, because so many\rfeatures are not necessary and redundant. Extracting a small number of good features will be an\rimportant issue, and it can be solved by using dimensionality reduction techniques. However, the popular\rdimensionality reduction method will suffer from the eigen-decomposition of dense matrix problem which\ris expensive in memory and time. We adopt unsupervised (unlabeled) spectral regression method for\rdimensionality reduction, which well avoids the problem of dense matrix eigen-decomposition problem\rand can be applied on large scale data sets. Histograms of Oriented Gradients (HOG) are robust features\rwhich not only well characterize the local shape and appearance but also show a certain degree of local\roptical and geometry invariance. In order to characterize the local shape and appearance better, we\rextract a three-tier pyramid HOG descriptor vector for one sample. Then we adopt the unsupervised\rspectral regression method for dimensionality reduction on these descriptor vectors. Our algorithm\rcan be applied in the library entrance guard system of university and other research fields. Several\rexperiments on well-known face databases have shown good performance and good invariance against\rillumination, occlusion and local deformation, etc.