Person:

Cha, Miriam

We introduce Deep Sparse-coded Network (DSN), a deep architecture based on sparse coding and dictionary learning. Key advantage of our approach is two-fold. By interlacing max pooling with sparse coding layer, we achieve nonlinear activation analogous to neural networks, but suffering less from diminished gradients. We use a novel backpropagation algorithm to finetune our DSN beyond the pretraining by layer-by-layer sparse coding and dictionary learning. We build an experimental 4-layer DSN with the 1-regularized LARS and greedy-0 OMP and demonstrate superior performance over deep stacked autoencoder on CIFAR-10.

Recent advances in clustering have shown that ensuring a minimum separation between cluster centroids leads to higher quality clusters compared to those found by methods that explicitly set the number of clusters to be found, such as k-means. One such algorithm is DP-means, which sets a distance parameter λ for the minimum separation. However, without knowing either the true number of clusters or the underlying true distribution, setting λ itself can be difficult, and poor choices in setting λ will negatively impact cluster quality. As a general solution for finding λ, in this paper we present λ-means, a clustering algorithm capable of deriving an optimal value for λ automatically. We contribute both a theoretically-motivated cluster-based version of λ-means, as well as a faster conflict-based version of λ-means. We demonstrate that λ-means discovers the true underlying value of λ asymptotically when run on datasets generated by a Dirichlet Process, and achieves competitive performance on a real world test dataset. Further, we demonstrate that when run on both parallel multicore computers and distributed cluster computers in the cloud, cluster-based λ-means achieves near perfect speedup, and while being a more efficient algorithm, conflict-based λmeans achieves speedups only a factor of two away from the maximum-possible.

As an unsupervised learning method, sparse coding can discover high-level representations for an input in a large variety of learning problems. Under semi-supervised settings, sparse coding is used to extract features for a supervised task such as classification. While sparse representations learned from unlabeled data independently of the supervised task perform well, we argue that sparse coding should also be built as a holistic learning unit optimizing on the supervised task objectives more explicitly. In this paper, we propose sparse-coded net, a feedforward model that integrates sparse coding and task-driven output layers, and describe training methods in detail. After pretraining a sparse-coded net via semi-supervised learning, we optimize its task-specific performance in a novel backpropagation algorithm that can traverse nonlinear feature pooling operators to update the dictionary. Thus, sparse-coded net can be applied to supervised dictionary learning. We evaluate sparse-coded net with classification problems in sound, image, and text data. The results confirm a significant improvement over semi-supervised learning as well as superior classification performance against deep stacked autoencoder neural network and GMM-SVM pipelines in small to medium-scale settings.