document: On interactive visualization of high-dimensional data using the hyperbolic plane We propose a novel projection based visualization method for high-dimensional datasets by combining concepts from MDS and the geometry of the hyperbolic spaces . Our approach Hyperbolic Multi-Dimensional Scaling ( H-MDS ) extends earlier work ( 7 ) using hyperbolic spaces for visualization of tree structures data ( `` hyperbolic tree browser '' ) . By borrowing concepts from multi-dimensional scaling we map proximity data directly into the 2-dimensional hyperbolic space ( H2 ) . This removes the restriction to `` quasihierarchical '' , graph-based data -- limiting previous work . Since a suitable distance function can convert all kinds of data to proximity ( or distance-based ) data this type of data can be considered the most general . We used the circular Poincaré model of the H2 which allows effective human-computer interaction : by moving the `` focus '' via mouse the user can navigate in the data without loosing the `` context '' . In H2 the `` fish-eye '' behavior originates not simply by a non-linear view transformation but rather by extraordinary , non-Euclidean properties of the H2 . Especially , the exponential growth of length and area of the underlying space makes the H2 a prime target for mapping hierarchical and ( now also ) high-dimensional data . We present several high-dimensional mapping examples including synthetic and real world data and a successful application for unstructured text . By analyzing and integrating multiple film critiques from news : rec . art . movies . reviews and the internet movie database , each movie becomes placed within the H2 . Here the idea is , that related films share more words in their reviews than unrelated . Their semantic proximity leads to a closer arrangement . The result is a kind of high-level content structured display allowing the user to explore the `` space of movies '' .
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