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Adaptive granularity in tensors: A quest for interpretable structure.

Ravdeep S Pasricha1, Ekta Gujral1, Evangelos E Papalexakis1

  • 1Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA, United States.

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Summary
This summary is machine-generated.

This study introduces adaptive granularity aggregation for tensors, enabling latent concept discovery from sparse, unstructured data. The ICEBREAKER algorithm efficiently creates high-quality tensors for improved data analysis.

Keywords:
multi-aspect datatemporal granularitytensortensor decompositionunsupervised learning

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Area of Science:

  • Data Science
  • Machine Learning
  • Tensor Analysis

Background:

  • Frequent data collection yields sparse, unstructured tensors.
  • Existing tensor decomposition methods struggle with such data.
  • Low utility of raw tensors limits interpretable structure extraction.

Purpose of the Study:

  • Introduce adaptive granularity aggregation for tensors.
  • Enable meaningful latent concept discovery from sparse point process data.
  • Address limitations of current tensor analysis for unstructured datasets.

Main Methods:

  • Formal problem definition for adaptive granularity aggregation.
  • Exploration of various
  • good structure
  • definitions.
  • Development of the ICEBREAKER greedy algorithm for efficient tensor construction.

Main Results:

  • Demonstrated prohibitive combinatorial complexity of optimal solutions.
  • ICEBREAKER algorithm locally maximizes structure quality.
  • Constructed high-quality tensors from synthetic, semi-synthetic, and real datasets.

Conclusions:

  • Adaptive granularity aggregation is a novel approach for tensor analysis.
  • ICEBREAKER provides an efficient method for creating structured tensors.
  • The method enhances the interpretability and utility of sparse, high-frequency data.