**5.2 Sparse PCA**

One of PCA significant flaws is that the PCs are dense in most circumstances, implying that the majority of the loadings are non-zero. The model is difficult to interpret since each significant component is a linear combination of all the original variables. However, each axis may correspond to a specific gene in machine learning tasks such as gene analytics. In such instances, one can readily analyse the model and comprehend the physical meaning of the loading and the PCs if the majority of the entries in the loadings are zeros. Sparse PCA is a variant of PCA that uses sparse loading to build interpretable models. In Sparse PCA, each PC is a linear combination of a subset of the original variables.
