Ways to Handle Noises
Noise consists of measuring errors, anomalies, or discrepancies in the information gathered. Handling noise is important because it might result in models that are unreliable and forecasts that are not correct.
- Data preprocessing: It consists of methods to improve the quality of the data and lessen noise from errors or inconsistencies, such as data cleaning, normalization, and outlier elimination.
- Fourier Transform:
- The Fourier Transform is a mathematical technique used to transform signals from the time or spatial domain to the frequency domain. In the context of noise removal, it can help identify and filter out noise by representing the signal as a combination of different frequencies. Relevant frequencies can be retained while noise frequencies can be filtered out.
- Constructive Learning:
- Constructive learning involves training a machine learning model to distinguish between clean and noisy data instances. This approach typically requires labeled data where the noise level is known. The model learns to classify instances as either clean or noisy, allowing for the removal of noisy data points from the dataset.
- Autoencoders:
- Autoencoders are neural network architectures that consist of an encoder and a decoder. The encoder compresses the input data into a lower-dimensional representation, while the decoder reconstructs the original data from this representation. Autoencoders can be trained to reconstruct clean signals while effectively filtering out noise during the reconstruction process.
- Principal Component Analysis (PCA):
- PCA is a dimensionality reduction technique that identifies the principal components of a dataset, which are orthogonal vectors that capture the maximum variance in the data. By projecting the data onto a reduced set of principal components, PCA can help reduce noise by focusing on the most informative dimensions of the data while discarding noise-related dimensions.
How to handle Noise in Machine learning?
Random or irrelevant data that intervene in learning’s is termed as noise.