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Related Concept Videos

Classification of Systems-II01:31

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 Systems-I01:26

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:
Aggregates Classification01:29

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...
Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Classification of Signals01:30

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...

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Updated: May 12, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

A linear support higher-order tensor machine for classification.

Zhifeng Hao1, Lifang He, Bingqian Chen

  • 1Faculty of Computer, Guangdong University of Technology, Guangzhou 510006, China. mazfhao@scut.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 27, 2013
PubMed
Summary
This summary is machine-generated.

A new linear support higher-order tensor machine (SHTM) offers faster training and higher accuracy for tensor classification tasks. This method avoids local minima issues common in support tensor machines (STM).

Related Experiment Videos

Last Updated: May 12, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

  • Machine Learning
  • Data Science
  • Computer Vision

Background:

  • Tensor classification is crucial for complex data analysis.
  • Existing methods like Support Tensor Machines (STM) face challenges with non-convex optimization, leading to slow training and local minima.
  • There is a need for efficient and accurate tensor classification algorithms.

Purpose of the Study:

  • To introduce a novel linear Support Higher-order Tensor Machine (SHTM) for improved tensor classification.
  • To address the limitations of existing iterative tensor learning machines.
  • To enhance both accuracy and training speed in tensor pattern recognition.

Main Methods:

  • Developed a linear Support Higher-order Tensor Machine (SHTM) by integrating linear C-support vector machine (C-SVM) principles with tensor rank-one decomposition.
  • SHTM is designed as a theoretical extension of linear C-SVM for tensor data.
  • When applied to vector data, SHTM simplifies to the standard C-SVM.

Main Results:

  • Experiments on face and gait recognition datasets demonstrate SHTM's effectiveness.
  • Statistical tests confirm SHTM significantly outperforms STM and C-SVM (RBF kernel) in test accuracy.
  • SHTM shows a notable improvement in training speed, particularly for higher-order tensors.

Conclusions:

  • SHTM presents a computationally efficient and accurate alternative for tensor classification.
  • The proposed method overcomes the time-consuming nature and local minima issues of traditional iterative approaches.
  • SHTM offers a significant performance advantage, especially in recognizing patterns within higher-order tensor data.