What is the abbreviation of LDK in foreigners?

LDK is an abbreviation of I, Donnot, and Konw. Meaning I don't know. Abbreviations are for ease of use, and are shortened and omitted from longer words.

When abbreviating the wilderness, it should be faithful to the original text, not changing the theme or central idea of the original text, and not changing the outline of the original text. It can also be said to be a shorthand for a longer name. Abbreviations must be followed in such a way that they do not distort the meaning of the words.

Abbreviations. Abbreviations refer to Chinese words that are shortened and omitted from longer Chinese words for ease of use. Abbreviations should be faithful to the original text, without changing the theme or central idea of the original text, and without changing the outline of the original text.

The Internet abbreviation buzzword is a mirror of social phenomena, and its birth reflects the social state and Internet fashion trend at that time. Its novelty, fashion, simplicity and convenience and vivid image are deeply loved by young people. But there are two sides to things, there is a good side and a bad side.

LDK is an abbreviation for I don not konw, which means I don't know.

Explanation of key vocabulary.

know English [n] American [no ]

v.Understand; Know; Know.

n.Informed.

Translation: I didn't know the truth until she told me what happened to Brother Cha.

Usage of know

know can be used as both an intransitive verb.

It can also be used as a transitive verb. When used as a transitive verb as "know Shimin, understand", it can be followed by nouns, pronouns, and infinitives with interrogative words that indicate things.

That or interrogative-led clause.

As an object, it can also be followed by "to be as+) n."/adj./prep. -phrase.Or the infinitive acts as a compound object of a complement.

When making the solution of "knowing, understanding", it can be used as an object with a noun that represents a person or thing. KNOW can be used in passive structures. know to be as an object complement.

, to be can often be omitted, but when the time to be precedes the time of know, the infinitive must be used and cannot be omitted.

The Chinese meaning of ld is linear dimensionality reduction.

Definition

LD is a commonly used data dimensionality reduction algorithm, which maps the data points in the original high-dimensional space to the low-dimensional space through mapping, while maintaining the relative distance relationship between the data to achieve the purpose of dimensionality reduction.

Through the high-dimensional transformation of single image data, the single image is transformed into a data set in high-dimensional space, and the one-dimensional representation vector of the eigenform structure of the high-dimensional data manifold is sought and used as the feature expression vector of the image data.

Thus, the high-dimensional image recognition problem is transformed into the recognition problem of feature expression vector, which greatly reduces the complexity of the calculation, reduces the recognition error caused by redundant information, and improves the accuracy of recognition.

advantages

When performing large-scale data processing, the raw data tends to have problems such as high redundancy and noise, and the use of LD algorithms can reduce the dimensionality of the dataset to better understand and process the data. In addition, the LD algorithm itself is simple and efficient, and is suitable for large-scale data dimensionality reduction, such as image recognition, speech recognition and other fields.

Common methods of algorithms

There are many different implementations of LD algorithms, such as PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), MDS (Multivariate Scale Analysis), etc. These methods can provide good interpretability and operability while ensuring the validity of the data.

4.Application examples

LD algorithm is widely used in the fields of big data analysis with spikes, feature selection and dimensionality reduction. For example, in the field of image processing, LD-based dimensionality reduction methods are used in mainstream face recognition algorithms, which map image information to a lower dimensional space for feature extraction. In addition, LD algorithms are also widely used in signal processing and pattern recognition.

In short, LD is a data dimensionality reduction algorithm, which achieves the purpose of dimensionality reduction by mapping data points in high-dimensional space to low-dimensional space, while maintaining the relative distance relationship between data. It has the advantages of simple calculation, high efficiency and good interpretability, and is widely used in the fields of big data analysis, signal processing, and pattern recognition.