Understanding Data Noise & Methods to Detect & Remove Noise in Datasets for Machine Learning
Data noise is one of the most valuable assets of modern businesses, and the quality of the data can significantly impact business decisions and outcomes.
However, data is often incomplete, inconsistent, or contains errors in real-world scenarios, known as data noise. This noise can lead to incorrect analyses and misleading results, ultimately impacting business decisions negatively.
Therefore, detecting and removing data noise is essential for ensuring data accuracy and reliability. In this blog, we will explore various methods to detect and remove data noise from datasets, helping businesses make informed decisions based on reliable data.
Understanding Data Noise
Noise is unwanted data items, features, or records which don’t help in explaining the feature itself, or the relationship between the feature & target.
In data science, removing noise from datasets is an essential step in preparing data for analysis, especially when working with large datasets that have high levels of noise.
Noise often causes the algorithms to miss out on patterns in the data. Noisy data is meaningless data. This terminology has been used as a synonym for corrupt data.
However, its meaning includes all data that machines cannot understand and interpret correctly, such as unstructured text. Any data that is received, stored or modified in a way that the program cannot read or use can be called noisy data.
Methods To Detect & Remove Noise in Dataset
Principal Component Analysis
Principal Component Analysis (PCA) is a mathematical method that uses the orthogonal property to transform a set of variables that may be interrelated into a set of uncorrelated variables. These new variables are commonly referred to as “principal components”.
By utilizing PCA, we can simplify complex datasets and extract meaningful information from them. The principal components represent the direction of maximum variance in the data and can be used to identify patterns or relationships between variables that might otherwise be difficult to discern.
This technique is frequently utilized in fields such as finance, biology, and engineering to improve data analysis and model development. PCA attempts to remove corrupted data from a signal or image using conservative noise while preserving key features.
PCA is an engineering and statistical method that reduces the dimensions of an input signal or data by projecting it onto different axes. For better understanding, imagine projecting a point along the X-axis to the XY dimension.
Noise floor: Y-axis can now be removed. This phenomenon is called “dimensional reduction”. Therefore, principal component analysis reduces noise in the input data by removing axes that contain noisy data.
Autoencoders can be used to remove noise; Random variants of the autoencoders are available. Since they can be trained to recognize noise detections in signals or data, they can be used as switches by feeding them noisy data and receiving clean data as output.
An autoencoder consists of two parts: an encoder that converts the input data into an encrypted state, and a decoder that decodes the encrypted state. The auto-noise reduction encoder does two things: it encodes the input while preserving as much detail as possible in the output. It also reflects the effects of noise randomly added to the input data.
The main purpose of the autoencoder is to drive the secret layer to learn more powerful features. The autoencoder is then trained to reconstruct the input data from the degraded version with minimal loss. An example is showing the use of the autoencoder to denoise a signal.
If you have a noisy dataset that includes large background patterns noise, which may not be relevant to a data scientist, you may need to clean the data. One approach to solving this issue is to use an adaptive noise cancellation technique, which eliminates the noisy signal. This method utilizes two signals: a target signal and a noise-free background signal.
By comparing the two signals, the adaptive noise cancellation technique can identify and remove any unwanted background noise. This is achieved by adjusting the filter coefficients in real-time to adapt to changes in the noise environment.
This approach is commonly used in applications where there is a need to remove unwanted noise from signals, such as in speech recognition or medical signal processing. Overall, this technique can be highly effective in cleaning up noisy datasets and improving data analysis.
Numerous studies have demonstrated so, that the signal or data possesses a discernible structure and that it is possible to eliminate noise directly from it. So, the Fourier Transform is employed in this process to convert the signal into the frequency domain.
Although the impact is not apparent in the original raw signal or data, analyzing the signal in the frequency domain reveals that the majority of signal information in the time domain can be represented by only a handful of frequencies. Since noise is erratic, it is dispersed across all frequencies.
So according to this principle, we can filter out the majority of noisy data by retaining the frequencies that contain the most significant signal information and discarding the remainder. It is feasible to remove noisy signals from the dataset using this approach.
Python Code for Removing Noise from Dataset
So now let’s see how we can implement these techniques in Python, for this tutorial, we will use the NumPy and OpenCV libraries.
First, let’s import the necessary libraries:
|import numpy as np|
Next, let’s load the image we want to process:
|img = cv2.imread(‘image.png’)|
1. Median Filtering: To apply median filtering, we use the cv2.medianBlur() function:
|median = cv2.medianBlur(img, 5)|
Here, we have used a kernel size of 5×5.
2. Mean Filtering: To apply to mean filtering, we use the cv2.blur() function:
|mean = cv2.blur(img, (5, 5))|
Here, we have used a kernel size of 5×5.
3. Gaussian Filtering: To apply Gaussian filtering, we use the cv2.GaussianBlur() function:
|gaussian = cv2.GaussianBlur(img, (5, 5), 0)|
Here, we have used a kernel size of 5×5 and a standard deviation of 0.
4. Fourier Transform: To apply Fourier Transform, we first convert the image to grayscale and then use the cv2.dft() function:
|import numpy as np|
img = cv2.imread(‘xfiles.jpg’,0)
img_float32 = np.float32(img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
dft = cv2.dft(np.float32(gray),
dft = cv2.dft(img_float32, flags = cv2.DFT_COMPLEX_OUTPUT)
dft_shift = np.fft.fftshift(dft)
magnitude_spectrum = 20*np.log(cv2.magnitude(dft_shift[:,:,0],dft_shift[:,:,1]))
Next, we apply a high-pass filter to remove the periodic noise:
|rows, cols = img.shape|
crow, ccol = rows/2, cols/2 # center
# create a mask first, center square is 1, remaining all zeros
mask = np.zeros((rows, cols, 2), np.uint8)
mask[crow-30:crow+30, ccol-30:ccol+30] = 1
Separating signal from noise is a major concern for today’s data scientists. It can lead to performance issues such as overfitting, which can cause machine learning algorithms to behave abnormally. Algorithms use noise for generalization.
Therefore, the safest approach is to remove or reduce noisy data from the signal or data set. It is important to note that none of the above methods handle noise perfectly.
So if possible, it is worth considering collecting new or more data to improve the signal-to-noise ratio of the source. Hence, “Garbage in, garbage out” is a well-known law of arithmetic. So, the faster the repair, the better the result.