  In this work, we propose a new data visualization and clustering technique
for discovering discriminative structures in high-dimensional data. This
technique, referred to as cPCA++, utilizes the fact that the interesting
features of a "target" dataset may be obscured by high variance components
during traditional PCA. By analyzing what is referred to as a "background"
dataset (i.e., one that exhibits the high variance principal components but not
the interesting structures), our technique is capable of efficiently
highlighting the structure that is unique to the "target" dataset. Similar to
another recently proposed algorithm called "contrastive PCA" (cPCA), the
proposed cPCA++ method identifies important dataset specific patterns that are
not detected by traditional PCA in a wide variety of settings. However, the
proposed cPCA++ method is significantly more efficient than cPCA, because it
does not require the parameter sweep in the latter approach. We applied the
cPCA++ method to the problem of image splicing localization. In this
application, we utilize authentic edges as the background dataset and the
spliced edges as the target dataset. The proposed method is significantly more
efficient than state-of-the-art methods, as the former does not require
iterative updates of filter weights via stochastic gradient descent and
backpropagation, nor the training of a classifier. Furthermore, the cPCA++
method is shown to provide performance scores comparable to the
state-of-the-art Multi-task Fully Convolutional Network (MFCN).
