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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Compression for Quadratic Similarity Queries.

Amir Ingber¹, Thomas Courtade¹, Tsachy Weissman¹

¹Department of Electrical Engineering, Stanford University, Stanford, CA 94305 USA.

IEEE Transactions on Information Theory

|January 30, 2018

Summary

This summary is machine-generated.

Performing similarity queries on compressed data requires exceeding an identification rate threshold for reliable results. Exceeding this rate ensures exponentially reliable query responses, crucial for data compression and analysis.

Keywords:

Compression databases error exponent identification rate search

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Area of Science:

Information Theory
Data Compression
Signal Processing

Background:

Similarity queries on compressed data present unique challenges.
Understanding the trade-offs between compression, sequence length, and query reliability is essential.

Purpose of the Study:

To analyze the fundamental trade-offs in performing similarity queries on compressed data.
To characterize the conditions for reliable query answering and the reliability exponent.

Main Methods:

Focus on the quadratic similarity measure.
Analysis of Gaussian sources to determine the identification rate threshold.
Characterization of the reliability exponent for compressed data queries.

Main Results:

Reliable similarity queries on compressed Gaussian data are possible if and only if the compression rate exceeds a defined identification rate.
Compression rates above the identification rate allow for exponentially reliable query responses.
The Gaussian source requires the highest compression rate for a given variance.

Conclusions:

The identification rate is a critical threshold for reliable similarity queries on compressed data.
Achieving high reliability in query responses is possible with sufficient compression rates.
A robust scheme is presented for attaining maximal compression rates across various source distributions.