Abstract The autoencoder is an artificial neural network that performs nonlinear dimension reduction and learns hidden representations of unlabeled data. With a linear transfer function it is similar to the principal component analysis (PCA). While both methods use weight vectors for linear transformations, the autoencoder does not come with any indication similar to the eigenvalues in PCA that are paired with eigenvectors. We propose a novel autoencoder node saliency method that examines whether the features constructed by autoencoders exhibit properties related to known class labels. The supervised node saliency ranks the nodes based on their capability of performing a learning task. It is coupled with the normalized entropy difference (NED). We establish a property for NED values to verify classifying behaviors among the top ranked nodes. By applying our methods to real datasets, we demonstrate their ability to provide indications on the performing nodes and explain the learned tasks in autoencoders.
One can also compute pairwise comparison of histograms. A review on measures for quantifying the difference between two histograms can be found in @cite_38 . @cite_34 presents an overview of histogram distance measures. The pairwise comparison is useful for detecting deviating signals. We are not interested in comparing two histograms at a time due to its cumbersome pairwise results and the wide selection of distance measures.
@cite_38 @cite_34
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Distance or similarity measures are essential to solve many pattern recognition problems such as classification, clustering, and retrieval problems. Various distance similarity measures that are applicable to compare two probability density functions, pdf in short, are reviewed and categorized in both syntactic and semantic relationships. A correlation coefficient and a hierarchical clustering technique are adopted to reveal similarities among numerous distance similarity measures. Human tumor–derived cell lines are indispensable tools for basic and translational oncology. They have an infinite life span and are easy to handle and scalable, and results can be obtained with high reproducibility. However, a tumor-derived cell line may not be authentic to the tumor of origin. Two major questions emerge: Have the identity of the donor and the actual tumor origin of the cell line been accurately determined? To what extent does the cell line reflect the phenotype of the tumor type of origin? The importance of these questions is greatest in translational research. We have examined these questions using genetic profiling and transcriptome analysis in human glioma cell lines. We find that the DNA profile of the widely used glioma cell line U87MG is different from that of the original cells and that it is likely to be a bona fide human glioblastoma cell line of unknown origin.