Ms. Green M&M (full rear view)

Full back view of Ms. Green M&M advertising icon. Candy dispenser, green M&M candy in rolling car.https://scholarscompass.vcu.edu/brandcenter_icons/1169/thumbnail.jp

Ms. Green M&M (full front view)

Full front view of Ms. Green M&M advertising icon. Candy dispenser, green M&M candy in rolling car.https://scholarscompass.vcu.edu/brandcenter_icons/1168/thumbnail.jp

Blue M&M Figure (full rear view)

Full back view of Blue M&M advertising icon. Plastic candy dispenser, blue M&M figure playing saxophone.https://scholarscompass.vcu.edu/brandcenter_icons/1102/thumbnail.jp

Red M&M Figure (full rear view)

Full back view of Red M&M advertising icon. Plastic red M&M figure.https://scholarscompass.vcu.edu/brandcenter_icons/1305/thumbnail.jp

Blue M&M Figure on Pedestal (full front view)

Full front view of Blue M&M advertising icon. Plastic candy dispenser, blue M&M figure playing saxophone on pedestal.https://scholarscompass.vcu.edu/brandcenter_icons/1121/thumbnail.jp

Blue M&M Figure on Pedestal (full rear view)

Full back view of Blue M&M advertising icon. Plastic candy dispenser, blue M&M figure playing saxophone on pedestal.https://scholarscompass.vcu.edu/brandcenter_icons/1122/thumbnail.jp

Red M&M Figure (full front view)

Full front view of Red M&M advertising icon. Plastic red M&M figure.https://scholarscompass.vcu.edu/brandcenter_icons/1304/thumbnail.jp

Blue M&M Figure (full front view)

Full front view of Blue M&M advertising icon. Plastic candy dispenser, blue M&M figure playing saxophone.https://scholarscompass.vcu.edu/brandcenter_icons/1101/thumbnail.jp

Adobe Systems

An important problem in many fields is the analysis of counts data to extract meaningful latent components. Methods like Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) have been proposed for this purpose. However, they are limited in the number of components they can extract and lack an explicit provision to control the “expressiveness ” of the extracted components. In this paper, we present a learning formulation to address these limitations by employing the notion of sparsity. We start with the PLSA framework and use an entropic prior in a maximum a posteriori formulation to enforce sparsity. We show that this allows the extraction of overcomplete sets of latent components which better characterize the data. We present experimental evidence of the utility of such representations.