Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Image Compression | Results Analysis
Principal Component Analysis
course content

Course Content

Principal Component Analysis

Principal Component Analysis

1. What is Principal Component Analysis
IntroductionPractical Application of PCAMathematical IdeaExamples of Real ProblemsHow to Explain the Obtained Results?
2. Basic Concepts of PCA
StandardizationCovariance MatrixEigenvalues and EigenvectorsFeature Vector and Principal ComponentsSeeing the Big Picture
3. Model Building
Scikit-learn for PCAExplore DatasetFit Data into the ModelChallenge
4. Results Analysis
Explain Resulting ComponentsWhat’s after?Data CompressionNoise ReductionImage Compression

Image Compression

Let's move on to the final task that PCA can solve - this is image compression. The solution of this problem occurs according to the same algorithm as usual. We already know how to create PCA models and load data into them. So now we will delve into other details. Compression of black and white and color images is done differently. Compressing black and white images is no different from compressing regular ones. While for color images it is required to: split the image into 3 RGB color channels, reduce the dimension of each channel using PCA and then combine the channels into a full-fledged color image. To read images and separate them into RGB channels, we need the matplotlib and cv2 libraries:

We standardize the data. We can implement this easier, without using a library, but only with the help of division:

Now let's create 3 PCA models:

Now we can combine the received data into one image:

Task

Reduce the dimension of the black and white image to 40 components.

Task

Reduce the dimension of the black and white image to 40 components.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 4. Chapter 5
toggle bottom row

Image Compression

Let's move on to the final task that PCA can solve - this is image compression. The solution of this problem occurs according to the same algorithm as usual. We already know how to create PCA models and load data into them. So now we will delve into other details. Compression of black and white and color images is done differently. Compressing black and white images is no different from compressing regular ones. While for color images it is required to: split the image into 3 RGB color channels, reduce the dimension of each channel using PCA and then combine the channels into a full-fledged color image. To read images and separate them into RGB channels, we need the matplotlib and cv2 libraries:

We standardize the data. We can implement this easier, without using a library, but only with the help of division:

Now let's create 3 PCA models:

Now we can combine the received data into one image:

Task

Reduce the dimension of the black and white image to 40 components.

Task

Reduce the dimension of the black and white image to 40 components.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 4. Chapter 5
toggle bottom row

Image Compression

Let's move on to the final task that PCA can solve - this is image compression. The solution of this problem occurs according to the same algorithm as usual. We already know how to create PCA models and load data into them. So now we will delve into other details. Compression of black and white and color images is done differently. Compressing black and white images is no different from compressing regular ones. While for color images it is required to: split the image into 3 RGB color channels, reduce the dimension of each channel using PCA and then combine the channels into a full-fledged color image. To read images and separate them into RGB channels, we need the matplotlib and cv2 libraries:

We standardize the data. We can implement this easier, without using a library, but only with the help of division:

Now let's create 3 PCA models:

Now we can combine the received data into one image:

Task

Reduce the dimension of the black and white image to 40 components.

Task

Reduce the dimension of the black and white image to 40 components.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Let's move on to the final task that PCA can solve - this is image compression. The solution of this problem occurs according to the same algorithm as usual. We already know how to create PCA models and load data into them. So now we will delve into other details. Compression of black and white and color images is done differently. Compressing black and white images is no different from compressing regular ones. While for color images it is required to: split the image into 3 RGB color channels, reduce the dimension of each channel using PCA and then combine the channels into a full-fledged color image. To read images and separate them into RGB channels, we need the matplotlib and cv2 libraries:

We standardize the data. We can implement this easier, without using a library, but only with the help of division:

Now let's create 3 PCA models:

Now we can combine the received data into one image:

Task

Reduce the dimension of the black and white image to 40 components.

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 4. Chapter 5
Switch to desktop for real-world practiceContinue from where you are using one of the options below
We're sorry to hear that something went wrong. What happened?
some-alt