BEIJING, March 25,
2024 /PRNewswire/ -- MicroAlgo Inc. (NASDAQ: MLGO)
(the "Company" or "MicroAlgo"), today announced that it developed a
deep clustering algorithm based on multi-level feature fusion.
Multi-level feature fusion refers to the fusion of different levels
of data features to obtain a richer representation of the features
and improve the clustering algorithm's ability to understand the
data, resulting in better clustering results. In deep clustering
algorithms, multiple features are usually used to describe the
data, such as low-level features of the original data and
high-level features after processing.
MicroAlgo Inc.'s deep clustering algorithm based on multi-level
feature fusion effectively solves the problems of data
dimensionality disaster and feature redundancy by extracting and
fusing features from data at different levels. It can automatically
discover hidden patterns and similarities in the data to cluster
the data points. Utilizing multi-level feature fusion and feature
information at different levels, can better mine the intrinsic
structure of the data and the relationship between the features,
and improve the accuracy and stability of the clustering algorithm.
At the same time, MicroAlgo Inc. used a combination of hierarchical
clustering and deep learning to achieve more accurate clustering
results. The specific process is as follows:
Feature extraction: Firstly, different levels of features of the
input data are extracted. These features can be the color, texture,
shape, etc. of the image. By extracting multiple features at
different levels, we can capture more details and different aspects
of the data.
Hierarchical clustering: Next, the extracted features are
clustered using a hierarchical clustering algorithm. Hierarchical
clustering is a bottom-up or top-down clustering method that can be
used to divide the data into different clusters based on their
similarity. The features at different levels are taken as input and
the data is clustered hierarchically by using a hierarchical
clustering algorithm.
Deep learning: To further improve the accuracy of clustering,
MicroAlgo Inc. utilized a deep learning method to learn a
representation of the data and input it as features into the
hierarchical clustering algorithm. Deep learning can better capture
the complex structure and features of data by mapping the data into
a higher dimensional representation space through multiple layers
of non-linear transformations.
Feature fusion: In the last, features obtained from different
levels and deep learning are fused. This can be achieved by simple
feature splicing, feature weighting, or feature fusion networks. By
fusing multiple features of different levels and types, fully using
the rich information from the data, to obtain more accurate and
comprehensive clustering results.
The deep clustering algorithm based on multi-level feature
fusion is widely used in image processing, natural language
processing, social network analysis, finance, healthcare and other
fields. For example, in image processing, the deep clustering
algorithm based on multi-level feature fusion can be used for tasks
such as image classification, target detection and image
segmentation. By clustering image features, automatic
classification and recognition of images is possible. In the field
of natural language processing, the deep clustering algorithm based
on multi-level feature fusion can be used for tasks such as text
clustering, sentiment analysis and text generation. By clustering
text, it can realize automatic classification and analysis of
large-scale text data. In social network analysis, the deep
clustering algorithm based on multi-level feature fusion can be
used for tasks such as user analysis and recommendation systems in
social networks. By clustering user behaviors, it can discover
correlations between users and provide personalized recommendation
services.
In the future, MicroAlgo Inc. will continue to conduct in-depth
research on the deep clustering algorithm based on multi-level
feature fusion and focus on researching more efficient feature
extraction methods, more flexible clustering algorithms, the
combination of deep clustering algorithms with other tasks, and the
modeling and handling of uncertainty, and other directions. Further
advancing the data pre-processing, feature selection, evaluation of
clustering results, and algorithmic interpretations by improving
the development and application of the deep clustering algorithm
based on multi-level feature fusion.
About MicroAlgo Inc.
MicroAlgo Inc. (the "MicroAlgo"), a Cayman
Islands exempted company, is dedicated to the development and
application of bespoke central processing algorithms. MicroAlgo
provides comprehensive solutions to customers by integrating
central processing algorithms with software or hardware, or both,
thereby helping them to increase the number of customers, improve
end-user satisfaction, achieve direct cost savings, reduce power
consumption, and achieve technical goals. The range of
MicroAlgo's services includes algorithm optimization,
accelerating computing power without the need for hardware
upgrades, lightweight data processing, and data intelligence
services. MicroAlgo's ability to efficiently deliver software and
hardware optimization to customers through bespoke central
processing algorithms serves as a driving force for MicroAlgo's
long-term development.
Forward-Looking Statements
This press release contains statements that may constitute
"forward-looking statements." Forward-looking statements are
subject to numerous conditions, many of which are beyond the
control of MicroAlgo, including those set forth in the Risk Factors
section of MicroAlgo's periodic reports on
Forms 10-K and 8-K filed with the SEC. Copies are
available on the SEC's website, www.sec.gov. Words such as
"expect," "estimate," "project," "budget," "forecast,"
"anticipate," "intend," "plan," "may," "will," "could," "should,"
"believes," "predicts," "potential," "continue," and similar
expressions are intended to identify such forward-looking
statements. These forward-looking statements include, without
limitation, MicroAlgo's expectations with respect to future
performance and anticipated financial impacts of the business
transaction.
MicroAlgo undertakes no obligation to update these statements
for revisions or changes after the date of this release, except as
may be required by law.
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SOURCE Microalgo.INC