sparse method

USAGE SUMMARY

The phrase "sparse method" is correct and usable in written English.
It can be used in contexts related to mathematics, computer science, or data analysis, where it refers to techniques that deal with sparse data or representations. Example: "The sparse method employed in this algorithm significantly reduces computational complexity when handling large datasets."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

12 human-written examples

This sparse method is more closely related to the statistical-based methods.

EURASIP Journal on Image and Video Processing

Thus, we propose a sparse method called to unmix spectra and apply it to explore the elucidate structural and spatial distribution of the plant cell well.

Neurocomputing

In our current sparse method, we make no assumption of the surface reflectance property and treat all non-Lambertian effects (specularity and shadow) equally.

EURASIP Journal on Image and Video Processing

This pyramidal version includes a multi-scale strategy which allows us to handle larger displacements while keeping the reduced computational load of Lucas-Kanade sparse method [22].

EURASIP Journal on Advances in Signal Processing

The algorithm may be associated with any sparse method, but clearly the accuracy of the final results will depend on the accuracy of the sparse approximation.

EURASIP Journal on Advances in Signal Processing

However, the initial values of the inlier map would directly influence the final result, whereas our sparse method does not depend on the choice of any prior.

EURASIP Journal on Image and Video Processing

Human-verified similar examples from authoritative sources

Similar Expressions

47 human-written examples

Fig. 14 Statistics for the angular error of normal maps recovered using sparse methods.

EURASIP Journal on Image and Video Processing

Fig. 15 Normal maps of the Venus dataset recovered using sparse methods.

EURASIP Journal on Image and Video Processing

We also investigated the performance of sparse methods under a highly biased lighting distribution.

EURASIP Journal on Image and Video Processing

We tested the performance of the sparse methods under various degrees of light calibration error on the Caesar dataset.

EURASIP Journal on Image and Video Processing

On the other hand, all sparse methods are quite insensitive to salt and pepper noise (Fig. 17).

EURASIP Journal on Image and Video Processing

Expert writing Tips

Best practice

When using the term "sparse method", clearly define what constitutes 'sparsity' in your specific context. This ensures that readers understand the criteria by which data or models are considered sparse.

Common error

Avoid assuming that all readers are familiar with the specific type of sparsity you are employing. Always provide sufficient context to avoid ambiguity. For example, while L1 regularization promotes sparsity, simply stating that you're using a "sparse method" might not be informative enough for a general audience.

Linguistic Context

The phrase "sparse method" functions as a noun phrase where the adjective "sparse" modifies the noun "method". This indicates a specific type of method characterized by sparsity, as supported by Ludwig's examples.

Expression frequency: Uncommon

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

The phrase "sparse method" is a technical term primarily used in scientific and academic contexts to describe methodologies that leverage sparsity. As Ludwig AI confirms, this phrase is grammatically correct and functions as a noun phrase, with "sparse" modifying "method". While not exceedingly common, its usage is consistent within its domain. When employing this term, clarity is key: ensure the specific understanding of 'sparsity' within your context is well-defined. Alternative phrases such as "sparse technique" or "sparsity-based method" may offer subtle nuances in emphasis.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

sparse technique

Replaces "method" with "technique", emphasizing the procedural aspect.

sparse approach

Substitutes "method" with "approach", highlighting the strategic or conceptual angle.

sparsity-based method

Emphasizes that the method is based on the principle of sparsity.

sparsification method

Focuses on the act of making something sparse.

sparse computational method

Specifies that the method is related to computation.

sparse algorithm

Replaces "method" with "algorithm", focusing on the set of rules a computer follows.

sparsely populated method

Highlights the characteristic of having few elements or features.

sparse modeling technique

Combines "sparse" with "modeling", indicating a technique for creating simplified models.

method based on sparsity

Rearranges the phrase to emphasize the foundational concept of sparsity.

limited variable method

Focuses on the use of a limited number of variables.

FAQs

How is a "sparse method" used in data analysis?

In data analysis, a "sparse method" typically involves techniques that focus on identifying and utilizing only the most relevant features or data points, effectively reducing the dimensionality and complexity of the analysis. This is useful for high-dimensional datasets where many features are irrelevant.

What are some alternatives to saying "sparse method"?

Depending on the specific context, you could use alternatives like "sparse technique", "sparse approach", or "sparsity-based method". The best choice depends on whether you want to emphasize the procedure, the strategy, or the underlying principle of sparsity.

What distinguishes a "sparse method" from a traditional method in machine learning?

Unlike traditional methods that may use all available features, a "sparse method" in machine learning deliberately selects a subset of features or parameters, often by applying regularization techniques like L1 regularization. This can lead to simpler models that generalize better to unseen data.

In what scenarios is a "sparse method" most appropriate?

A "sparse method" is particularly useful when dealing with high-dimensional data, such as in genomics or image processing, where the number of features far exceeds the number of samples. It helps in feature selection, reduces overfitting, and improves the interpretability of models.