# Occam's Razor for _Big Data_? On Detecting Quality in Large Unstructured Datasets
## 3 Big Data Analytics: From Machine Learning to Artificial Intelligence
### SOM for Single-Pixel Change Detection in Large Sets of Image Data  
In the image and vision sciences, at the intersection between machine learning algorithms for computer vision and computational neuroscience, low-level artificial neural networks have been proposed well before the area of big data and deep learning neural networks. The earliest AI approach in this context is, as explained in Section 3 herein, the self-organizing map or SOM [31; 32; 33; 86; 87], is largely inspired by the functional properties of visual neurons in the non-human and human primate [88; 89; 90; 91], and by psychophysical data on human detection [92; 93; 94; 95; 96; 97; 98; 99; 100; 101]. How elementary output parameters of the SOM may be exploited for detecting certain qualities in large, arbitrarily archived image datasets may be illustrated here on the example of rapid, unsupervised detection of strictly local changes invisible to humans in image time series. Such local changes in images may reveal different meaningful states of a physiological structure, tissue, or cell, and reflect progression or recession of a pathology, or the progressive response of a cell structure to treatment, not detectable by any of the currently available medical image analysis tools. A simple functional architecture of the self-organizing map may be applied to the unsupervised classification of massive amounts of patient data from different disease entities ranging from inflammation to cancer, as shown recently [35]. Other recent work [102; 103; 104; 105] has shown the quantization error (\(QE\)) in the output of a basic self-organized neural network map (\(SOM\)); in short, the SOM-QE is a parsimonious and highly reliable measure of the smallest local change in contrast or color data in random-dot, medical, satellite, and other potentially large image data. The SOM is easily implemented, learns the pixel structure of any target image in about two seconds by unsupervised \"winner-take-all\" learning, and detects local changes in contrast or color in a series of subsequent input images with a to-the-single-pixel precision, in less than two seconds for a set of 20 images [106; 107]. The QE in the SOM output permits to scale the spatial magnitude and the direction of change (+ or -) in local pixel contrast or color with a reliability that exceeds that of any human expert [102]. Applied to the automatic classification of arbitrarily archived scanning electron microscopy (SEM) images of CD4\({}^{+}\) T-lymphocytes (so-called _helper cells_) with varying extent of HIV virion infection [101], a four-by-four SOM (for an illustration, see Figure 3) may be trained on any image of a given series, whatever its size, using unsupervised winner-take-all learning. The functional architecture of the SOM is minimal, with a constant number of neurons and a constant neighborhood radius [31; 32; 33; 86; 87]. Winner-take-all learning and subsequent SOM processing to generate QE output for the automatic classification of images from a series of 20 takes less than two seconds. Each of the images from the series is associated with a SOM-QE output value. These values are then automatically listed in ascending order of magnitude on the y-axis, the image corresponding to each QE value is ranked on the x-axis as function of its SOM-QE label. This analysis achieved a 100% correct classification of the CD4\({}^{+}\) cell images in the order of the magnitude of HIV virion infection displayed in an image. A human expert electron microscopist with more than 25 years of experience took 52 min to sort the color-enhanced micrographs in the correct order, and 69 min to roughly sort the original grayscale images, but not with 100% correct. Moreover, for the human expert to be able to perform such a task at all, it is necessary to visualize all the images of a given series simultaneously on the computer screen, using software that allows zooming in and out of single images for close-up two-by-two comparisons.