Machine learning can effectively analyze data with several dimensions. However, it becomes complex to develop relevant models as the number of dimensions significantly increases. You will get abnormal results when you try to analyze data in high-dimensional spaces. This situation refers to the curse of dimensionality in machine learning. It depicts the need for more computational efforts to process and analyze a machine-learning model.
Let’s first understand what dimensions mean in this context.
What are Dimensions?
Dimensions are features that may be dependent or independent. The concept of dimensions in context to the curse of dimensionality becomes easier to understand with the help of an example. Suppose there is a dataset with 100 features. Now let’s assume you intend to build various separate machine learning models from this dataset. The models can be model-1, model-2, …. model-100. The difference between these models is the number of features.
Suppose we build model-1 with 3 features and model-2 with 5 features (both models have the same dataset). The model-2 has more information than model-1 because its number of features is comparatively higher. So, the accuracy of model-2 is more than that of model-1.
With the increase in the number of features, the model’s accuracy increases. However, after a specific threshold value, the model’s accuracy will not increase, although the number of features increases. This is because a model is fed with a lot of information, making it incompetent to train with correct information.
The phenomenon when a machine learning model’s accuracy decreases, although increasing the number of features after a certain threshold, is called the curse of dimensionality.
Why is it challenging to analyze high-dimensional data?
Humans are ineffective at finding patterns that may be spanned over many dimensions. When more dimensions are added to a machine learning model, the processing power required for the data analysis increases. Moreover, adding more dimensions increases the amount of training data needed to make purposeful data models.
The curse of dimensionality in machine learning is defined as follows,
As the number of dimensions or features increases, the amount of data needed to generalize the machine learning model accurately increases exponentially. The increase in dimensions makes the data sparse, and it increases the difficulty of generalizing the model. More training data is needed to generalize that model better.
The higher dimensions lead to equidistant separation between points. The higher the dimensions, the more difficult it will be to sample from because the sampling loses its randomness.
It becomes harder to collect observations if there are plenty of features. These dimensions make all observations in the dataset to be equidistant from all other observations. The clustering uses Euclidean distance to measure the similarity between the observations. The meaningful clusters can’t be formed if the distances are equidistant.
How to solve the curse of dimensionality?
The following methods can solve the curse of dimensionality.
1) Hughes Phenomenon
The Hughes Phenomenon states that with the increase in the number of features, the classifier’s performance also increases until the optimal number of features is attained. The classifier’s performance degrades when more features are added according to the training sets’ size.
Let’s understand the Hughes Phenomenon with an example. Suppose a dataset consists of all the binary features. We also suppose that the dimensionality is 4, meaning there are 4 features. In this case, the number of data points is 2^4 =16.
If the dimensionality is 5, the number of data points will be 2^5 = 1024. These examples indicate that the number of data points exponentially increases with the dimensionality. So, the number of data points that a machine learning model needs for training is directly proportional to the dimensionality.
From the Hughes Phenomenon, it is concluded that for a fixed-sized dataset, the increment in dimensionality leads to reduced performance of a machine learning model.
The solution to the Hughes Phenomenon is Dimensionality Reduction.
“Dimensionality Reduction” is the data conversion from a high-dimensional into a low-dimensional space. The idea behind this conversion is to let the low-dimensional representation hold some significant properties of the data. These properties will be almost identical to the data’s natural dimensions. Alternatively, it suggests decreasing the dataset’s dimensions.
How does Dimensionality Reduction help solve the Curse of Dimensionality?
- It decreases the dataset’s dimensions and thus decreases the storage space.
- It significantly decreases the computation time. This is because less number of dimensions need less computing time, and ultimately the algorithms train faster than before.
- It improves models’ accuracy.
- It decreases multicollinearity.
- It simplifies the data visualization process. Moreover, it easily identifies a meaningful pattern in the dataset because visualization in 1D/2D/3D space is quite simpler than visualization of more dimensions.
Note that Dimensionality Reduction is categorized into two types, i.e., Feature Selection and Feature Extraction.
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2) Deep Learning Technique
Deep learning doesn’t encounter the same concerns as other machine learning algorithms when dealing with high-dimensionality applications. This fact makes Neural Network modeling quite effective. Neural Network’s resistance to the curse of dimensionality proves to be quite useful in big data.
The Manifold Hypothesis is one theory that justifies how deep learning solves the curse of dimensionality in data mining. This theory implies that high dimensional data overlaps on a lower dimensional manifold that is equipped in a higher dimensional space.
It implies that in the high-dimensional data, there exists a fundamental pattern in lower-level dimensions that deep learning techniques can effectively manipulate. Hence, for a high-dimensional matrix, neural networks can efficiently find low-dimensional features that don’t exist in the high-dimensional space.
3) Use of Cosine similarity:
The effect of high dimensions in the curse of dimensionality in data mining can be reduced by uniquely measuring distance in a space vector. Specifically, you can use cosine similarity to substitute Euclidean distance. Cosine similarity presents less impact on data in higher dimensional spaces. Cosine similarity is extensively used in word-to-vec, TF-IDF, etc.
Cosine similarity assumes that the observations are made by assuming that the points are spread randomly and uniformly. If the points are not uniformly and randomly organized, the following conditions must be considered.
i) The effect of dimensionality is high when the points are densely located, and dimensionality is high.
ii) The effect of dimensionality is low when the points are sparsely located, and dimensionality is high.
One of the conventional tools capable of solving the curse of dimensionality is PCA (Principal Component Analysis). It converts the data into the most useful space. Hence, it enables the use of lesser dimensions that are quite instructive than the original data. In the pre-processing stage, the nonlinear relations between the initial data components may not be maintained because PCA is a linear tool.
In other words, PCA is a linear dimensionality reduction algorithm that lets you extract a new set of variables from a huge set of variables known as Principal Components.
It is important to note how Principal components are extracted. The first principal component describes the maximum variance in the dataset. The second principal component describes other variances in the dataset; it is unrelated to the first principal component. The third principal component describes the variance that is not described by the first and second principal components.
In a nutshell, PCA finds the best linear combinations of the variables to ensure the spread of points or the variance across the new variable is maximum.
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With the increase in the number of dimensions, the analysis and generalization of a machine learning model become difficult. It demands more computational efforts for its processing. The solutions discussed above can depend on the type of machine learning model and the type of application.