Finding your way in a multi-dimensional semantic space with luminoso
Robert E. Speer, Catherine Havasi, K. Nichole Treadway, Henry Lieberman · 2010
In AI, we often need to make sense of data that can be measured in many different dimensions -- thousands of dimensions or more -- especially when this data represents natural language semantics. Dimensionality reduction techniques can make this kind of data more understandable and more powerful, by projecting the data into a space of many fewer dimensions, which are suggested by the computer. Still, frequently, these results require more dimensions than the human mind can grasp at once to represent all the meaningful distinctions in the data.