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