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Found 8 papers, 1 papers with code
Coarse- and fine-scale geometric information content of Multiclass Classification and implied Data-driven Intelligence
Under any Multiclass Classification (MCC) setting defined by a collection of labeled point-cloud specified by a feature-set, we extract only stochastic partial orderings from all possible triplets of point-cloud without explicitly measuring the three cloud-to-cloud distances.
Unraveling S&P500 stock volatility and networks -- An encoding-and-decoding approach
In the real data applications, we introduce the application of our approach in forecasting stock returns.
Categorical exploratory data analysis on goodness-of-fit issues
Such annotations can critically shed invaluable light on validity as well as limitations of statistical modeling as a data analysis approach.
Extreme-K categorical samples problem
Upon the data matrix's row-axis, we discuss the pros and cons of Euclidean distance against its weighted version for building a binary clustering tree geometry.
Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images
Targeted color-dots with varying shapes and sizes in images are first exhaustively identified, and then their multiscale 2D geometric patterns are extracted for testing spatial uniformness in a progressive fashion.
From learning gait signatures of many individuals to reconstructing gait dynamics of one single individual
With proper color-coding and stacking, we reconstruct and represent an individual's gait dynamics via a 3D cylinder to collectively reveal universal deterministic and stochastic structural patterns on centisecond (10 milliseconds) scale across all rhythmic cycles.
Learning from Group Comparisons: Exploiting Higher Order Interactions
We study the problem of learning from group comparisons, with applications in predicting outcomes of sports and online games.
Complexity of Possibly-gapped Histogram and Analysis of Histogram (ANOHT)
Without unrealistic continuity and smoothness assumptions on a distributional density of one dimensional dataset, constructing an authentic possibly-gapped histogram becomes rather complex.
Methodology
