instances are modeled further away dictionary learning an approach known as dictionary learning involves learning the sparse representation of the underlying data these representative elements are simplebinary vectors (zeros and ones)and each instance in the dataset can be reconstructed as weighted sum of the representative elements the matrix (known as the dictionarythat this unsupervised learning generates is mostly populated by zeros with only few nonzero weights by creating such dictionarythis algorithm is able to efficiently identify the most salient representative elements of the original feature space--these are the ones that have the most nonzero weights the representative elements that are less important will have few nonzero weights as with pcadictionary learning is excellent for learning the underlying structure of the datawhich will be helpful in separating the data and in identifying interesting patterns independent component analysis one common problem with unlabeled data is that there are many independent signals embedded together into the features we are given using independent component analysis (ica)we can separate these blended signals into their individual components after the separation is completewe can reconstruct any of the original features by adding together some combination of the individual components we generate ica is commonly used in signal processing tasks (for exampleto identify the individual voices in an audio clip of busy coffeehouselatent dirichlet allocation unsupervised learning can also explain dataset by learning why some parts of the dataset are similar to each other this requires learning unobserved elements within the dataset--an approach known as latent dirichlet allocation (ldafor exampleconsider document of text with manymany words these words within document are not purely randomratherthey exhibit some structure this structure can be modeled as unobserved elements known as topics after traininglda is able to explain given document with small set of topicswhere for each topic there is small set of frequently used words this is the hidden structure the lda is able to capturehelping us better explain previously unstructured corpus of text closer look at unsupervised algorithms