PCA (Principal Component Analysis) and TSNE (T-Distribution Stochastic Neighborhood Embedding) are two popular techniques used to reduce the dimensionality of data in data analysis and machine Xi.
As useful as they are, they also have some limitations, which are explained below:
Linear nature: PCA is a linear method, which means that it can only capture linear relationships between variables. It may not be suitable for data with non-linear relationships, as PCA may not be able to capture underlying patterns in the data. TSNE, on the other hand, is a nonlinear method that captures more complex relationships between variables.
Information loss: PCA and TSNE are both techniques for reducing the dimensionality of data by projecting it into a low-dimensional space. This projection can lead to a loss of information, making it difficult to interpret the results or use the data for downstream tasks.
Parameter sensitivity: Both PCA and TSNE have several parameters that require careful selection to achieve the best results. The performance of these techniques may be sensitive to the selection of these parameters, and the optimal parameters may vary depending on a particular data set.
Computationally intensive: TSNE is more computationally intensive than PCA, especially when working with large datasets. This may limit the size of datasets that can be effectively analyzed with TSNE.
Not easy to explain: PCA and TSNE are both unsupervised techniques, which means they don't take into account the class labels of data points. As a result, the output of these techniques may be difficult to interpret and may not be directly applicable to classification or other supervisory Xi tasks.
Overfitting: Both PCA and TSNE can suffer from overfitting, especially if the dimensionality in the reduced space is too small. This can cause results to lose generalization of new data.
Overall, while PCA and TSNE can be useful techniques for reducing data dimensions and visualizing high-dimensional data, they should be used with caution and their limitations should be considered when applying them to different datasets.