Matrix singular value decomposition – information compression and noise reduction (open_cv) ? The blogger took a mathematical modeling class today, and the teacher talked aboutThe singular value decomposition of a matrix can be used for image compression. When bloggers learned linear algebra, they did not expect that singular value decomposition could be used in this […]
Tag: reduction
Python code for rough set knowledge reduction
I saw many people leaving messages to me about rough sets. Because the previous code took too long to write and was too simplified, I even forgot how to write it, so I didn’t respond to everyone. Now update the new version of rough set code Knowledge reduction is also quite simple. Just call RoughSets.cores […]
[SCM Graduation Project] [hj-014] Construction site dust reduction | Dust detection | Dust reduction treatment system | PM2.5 detection
1. Basic introduction Item name: Design of construction site dust reduction system based on microcontroller Design of dust detection system based on single chip microcomputer Design of dust reduction processing system based on single-chip microcomputer Design of PM2.5 detection system based on microcontroller Project number: mcuclub-hj-014 Microcontroller type: STM32F103C8T6 Specific functions: 1. Detect dust concentration […]
Comparison of three powerful dimensionality reduction techniques
Three main dimensionality reduction techniques PCA, LCA, SVD Here we will delve into three powerful dimensionality reduction techniques – Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Singular Value Decomposition (SVD). We not only introduce the basic algorithms of these methods, but also provide the advantages and disadvantages of each. As data sets grow […]
[Text Feature Representation (4)] Embedding is applied in clustering systems, using PCA for dimensionality reduction
What is clustering Clustering means clustering samples that are close to each other. The essence is to use a representation and similarity measure to process text. For example, we have a large amount of unclassified text. If we can know how many categories there are in advance, we can use the clustering method to roughly […]
[java] Han Shunping house rental system (array expansion and reduction)
Table of Contents Preface code HouseRentApp HouseView House HouseService Utility postscript Foreword A small project by Han Shunping at station b completed the expansion and reduction of the array. The codes for some functions are inconsistent with those in the video. Although there is a problem that the output content is not neat, it doesn’t […]
Opencv image scaling (enlargement, reduction), flipping, rotating
Article directory Opencv image scaling (enlargement, reduction), flipping, rotating 1. Why interpolation is needed during image scaling and rotation: 2. Common interpolation algorithms include: 3. Image scaling, flipping, and rotating: (1) Image scaling cv2::resize(), a function used to change the size of an image. It can be used for image enlargement and reduction operations: Function […]
Feature dimensionality reduction using feature extraction
1. Use principal components for feature dimensionality reduction For a given set of features, reduce the number of features while retaining the amount of information. from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn import datasets digits=datasets.load_digits() features=StandardScaler().fit_transform(digits.data) #Create a PCA that can retain 99% of the information (expressed as variance) pca=PCA(n_components=0.99,whiten=True) features_pca=pca.fit_transform(features) print(“Original […]
RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one.
pytorch multi-GPU (single machine multi-card) training pit: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. Problem Description Question exploration forward outputs multiple parameters but does not participate in loss calculation and will not cause an error. The key to the error is that some layers are defined in […]
WIFI signal status information CSI Data Processing Chapter Noise Reduction (3)
This article mainly talks about how to denoise the original data that contains a lot of noise after reading the data. Three noise reduction algorithms are introduced, Hampel extreme point removal, low-pass filtering, and wavelet transform. This tutorial refers to the article published on Ebaina Fall detection based on Wi-Fi (2): Data preprocessing_Column_Ebaina Technology Community […]