sparse characteristics

USAGE SUMMARY

The phrase "sparse characteristics" is correct and usable in written English.
It can be used to describe features or traits that are minimal or not abundant in a particular context. Example: "The sparse characteristics of the landscape made it appear desolate and uninviting."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

14 human-written examples

For the proposed BDSAE approach, the sparse characteristics of spectral amplitudes of speech signal are considered.

EURASIP Journal on Audio, Speech, and Music Processing

That is, both inter-cluster and intra-cluster models may exhibit the sparse characteristics.

EURASIP Journal on Wireless Communications and Networking

The sparse characteristics of correlated datasets have also been recently considered for transmission of EEG signals [17, 18].

EURASIP Journal on Advances in Signal Processing

More and more experiments have indicated the particularly sparse characteristics of wireless propagation channels, especially for the systems with enormous bandwidth.

EURASIP Journal on Wireless Communications and Networking

Many publications in the literature cast the CS into radar imaging [11 16], where they analyzed the sparse characteristics of SAR signals in different domains.

EURASIP Journal on Advances in Signal Processing

The impulses induced by bearing faults have convolutional sparse characteristics, and can be described as the convolution of shock responses and time location coefficients.

Measurement

Human-verified similar examples from authoritative sources

Similar Expressions

46 human-written examples

This sparse characteristic of ISAR images can be exploited to achieve SR.

EURASIP Journal on Advances in Signal Processing

And there are some requirements for the sparse characteristic of the received signals based on CS [8 11].

EURASIP Journal on Advances in Signal Processing

In addition, to guarantee the sparse characteristic of x, the L1 norm also makes the problem as a convex optimization problem.

EURASIP Journal on Advances in Signal Processing

Based on this general model, considering the sparse characteristic of the scene, we will construct the corresponding orthogonal CS matrix for the SAR imaging.

EURASIP Journal on Advances in Signal Processing

From this result we can see that most elements of this project matrix are near to zero, which accords with the sparse constraint of NTPCA. Figure 4(b) gives several samples for coefficients of feature vector after projection, which also prove the sparse characteristic of feature.

EURASIP Journal on Audio, Speech, and Music Processing

Expert writing Tips

Best practice

When describing data, use "sparse characteristics" to highlight that only a small amount of data significantly influences the outcome, as seen in signal processing and image analysis contexts.

Common error

Avoid assuming that "sparse characteristics" are insignificant. In many applications, such as compressed sensing, these characteristics are crucial for efficient data representation and processing.

Linguistic Context

The phrase "sparse characteristics" functions as a descriptive term, typically used to characterize data, signals, or systems where only a small subset of elements or features are significant. Ludwig AI confirms its usability in technical contexts.

Expression frequency: Uncommon

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, "sparse characteristics" is a grammatically sound phrase primarily used in scientific and technical domains to describe systems or data sets where only a small number of elements hold significant value. Ludwig AI indicates that this phrase is correct and usable. Alternatives include "limited attributes" or "scant features", though the specific nuance may vary. The phrase is crucial in contexts like signal processing and image analysis, where understanding the "sparse characteristics" enables efficient data handling and analysis. While its use isn't pervasive, its precision makes it essential in specialized fields.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

limited attributes

Replaces "sparse" with "limited", focusing on the restricted number of attributes.

scant features

Substitutes "sparse" with "scant", emphasizing the insufficiency or lack of features.

meager properties

Replaces "sparse" with "meager", highlighting the inadequacy or poor quality of the properties.

reduced traits

Substitutes "sparse" with "reduced", indicating a decrease in the quantity or extent of traits.

minimal qualities

Replaces "sparse" with "minimal", focusing on the least possible amount of qualities present.

infrequent attributes

Replaces "sparse" with "infrequent", highlighting the rare occurrence of the attributes.

scarce qualities

Substitutes "sparse" with "scarce", emphasizing the limited availability of the qualities.

thin features

Replaces "sparse" with "thin", highlighting the lack of density of the features.

scattered attributes

Substitutes "sparse" with "scattered", emphasizing the dispersed nature of the attributes.

isolated properties

Replaces "sparse" with "isolated", highlighting the separation of the properties.

FAQs

How can I use "sparse characteristics" in a sentence?

You can use "sparse characteristics" to describe data or signals where only a few elements are significant, such as "The image's "sparse characteristics" allowed for efficient compression".

What is an alternative to using "sparse characteristics"?

Alternatives include "limited attributes", "scant features", or "meager properties", depending on the specific context.

In what contexts are "sparse characteristics" typically observed?

"Sparse characteristics" are commonly observed in signal processing, image analysis, and machine learning, where data often has a structure that allows for efficient representation using only a few significant components.

Why are "sparse characteristics" important in data analysis?

"Sparse characteristics" enable efficient data compression, noise reduction, and feature extraction, leading to improved performance and reduced computational complexity in various applications.