6 research outputs found
Ratio of the number of pairs of images above the bend to the number of pairs of images below the bend in NV’s correlation dimension plot as a function of binned time difference plotted on log-log axes.
<p>The ratio is approximately 1 for a time difference bin of 21 to 34 mins which lies in the range of median and mean context durations (see text for details). The drop in ratio for certain time differences, marked with the rectangles, are signatures of periodicities in the data where recurrent visits to the same context spaced by those time differences contribute more pairs to the lower scale of the correlation dimension plot thereby decreasing the ratio. Note that the second time difference bin  =  [73,118) seconds and there are no transitions with these time differences in the data - images were captured by NV’s lifelogging device at approximately equal intervals of ∼60 seconds. The minimum time interval in the data is 59 seconds followed by 60, 61, 62 and 119 seconds.</p
Recurrence plot for NV’s images.
<p>The images are ordered in time on both X and Y axes. The substantial dark structure around the diagonal implies that similar visual contexts were visited close in time. The off-diagonal structures represent recurrent visits to similar visual contexts at different points in time.</p
The correlation dimension plot for NV’s images shows a two-scaled geometry.
<p>The bent-cable regression lower scale correlation dimension estimate is 6.06 and the top scale correlation dimension estimate is 14.27.</p
Takens’ delay embedding procedure: Recovery of the lower scale correlation dimension estimate for NV’s images.
<p>A time delay of τ = 10 minutes was used to construct the delay embedded vectors at each value of embedding dimension. As the embedding dimension is increased, the correlation dimension of the reconstructed delay embedded vectors asymptotes to the original lower scale estimate of 6.06.</p
Repeat Takens’ delay embedding procedure as in Fig. 4, but with a randomly permuted time series of NV’s images.
<p>As the embedding dimension is increased, the correlation dimension of the reconstructed delay embedded vectors keeps rising and never asymptotes, demonstrating that the dimensional structure of the data is dynamic in origin.</p