When data is presented that way, it is often because all 100 values in the vector should be read as one. For example, those can be a set of 100 measurements of, say, a temperature at different locations of a system. Data in the vector is likely to be correlated and the order of sub-features may be a valuable information (for instance, in my previous examples, if they represent the temperature measurements over a range of positions along a pipe).
If data is not correlated at all, or sub-features have nothing relevant in common, you can just expand each sub-feature as a normal feature. In the process, you may get a final dataset with numerous features, this will eventually need to reduce the dimension.
If data is deemed "simply" correlated, a good option would be to reduce the dimension of the vector from 100 to a much lower value, then expand it as before. The most straightforward way to do so is PCA. Be sure to perform it only over each particular multi-sub-features feature, at first.
If data is correlated and the order of features matters (i.e. $(1, 1, 0, 0, 0, 0)$ is closer to $(0, 1, 1, 0, 0, 0)$ than to $(0, 0, 1, 0, 0, 1)$, for instance, you will need a more complex approach. You could perform density-based clustering based on a custom distance function such as the Wasserstein metric / earth mover's distance. The 100-dimension feature would thus be reduced to a single feature representing a class among this clustered dataset.