Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

Classification of Leukocytes

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

The problem of over-medicalisation: How AOD disease models perpetuate inequity for young people with multiple disadvantage.

The International journal on drug policy·2022

Same author

Mixed anaerobic thoracic empyema: the first report of <i>Filifactor alocis</i> causing extra-oral disease.

New microbes and new infections·2019

Same author

The Detection and Estimation of Lactose in Urine.

Edinburgh medical journal·2018

Same author

Intra-operative endotracheal tube stabilisation for facial burns.

Burns : journal of the International Society for Burn Injuries·2010

Same author

Weighted universal image compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008

Same author

JPEG-compliant perceptual coding for a grayscale image printing pipeline.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008

Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 29, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Minimum cross-entropy pattern classification and cluster analysis.

J E Shore¹, R M Gray

¹SENIOR MEMBER, IEEE, Information Technology Division, Naval Research Laboratory, Washington, DC 20375.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

This study introduces a novel nearest neighbor classification using a non-Euclidean distortion measure based on cross-entropy minimization. This method offers optimal classification and simplifies cluster centroid computation.

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Related Experiment Videos

Last Updated: May 29, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Information Theory
Machine Learning
Pattern Recognition

Background:

Nearest neighbor rules are common for classifying measurement vectors using fixed reference vectors.
Existing methods often rely on Euclidean metrics, which may not be optimal for all data types.
Vector quantization techniques are used in areas like speech coding.

Purpose of the Study:

To develop a computationally attractive and optimal classification method using a non-Euclidean distortion measure.
To refine Kullback's classification method by exploiting properties of cross-entropy.
To generalize existing speech coding techniques.

Main Methods:

Utilizing a non-Euclidean information-theoretic distortion measure, specifically cross-entropy minimization.
Applying a nearest neighbor rule to a fixed set of characteristic feature vectors.
Leveraging minimum cross-entropy densities for refinement.

Main Results:

The proposed method achieves optimal classification in a well-defined sense.
The distortion measure is computationally attractive and simplifies cluster centroid calculation.
The approach generalizes speech coding by vector quantization.

Conclusions:

Cross-entropy minimization provides an effective framework for nearest neighbor classification.
This method offers advantages in computational efficiency and optimality.
The technique has broader applications beyond speech coding, including general vector classification.