Towards Information-Theoretic Visualization Evaluation Measure: A Practical example for Bertin's Matrices
From BELIV 2010
Presentation
Notes
- complexity of visualization with shortest length
- information theory, size of description is a measure of goodness, for matrix, can swap rows to get a better compression
- future work: pixel based technique, need 1:1 and reversible and lossless mapping between original data
q and a:
- data dimensions can be reordered, quantitative cannot be reordered; time series, series of number, but can make spiral of numbers, problem is to encode and map the data into a matrix
- adjusting the phase, reordering? only find out seasonality, but not more
- kol complexity, dealing with something that is connected to perception, not necc correlated to gzip size, agree about technique, but not based on any description of language, need to take perception into account; agree, universal description language, measure the number of bits to encode
- lossless encoding, affect best (yes), how about lossy compressions? Depends if the outliers are important, come closer to perception. Lossless is extremely important, but need to reverse
- get away from subjective vs math preference; all measures included in kol complexity
- not fundamental math complexity?
