We address the problem of signal denoising and pattern recognition in processing batch-mode time-series data by combining linear time-invariant filters, orthogonal multiresolution representations, sparsity-based methods. propose a novel approach to designing higher-order zero-phase low-pass, high-pass, band-pass infinite impulse response filters as matrices, using spectral transformation state-...

Sparsity often leads to efficient and interpretable representations for data. In this paper, we introduce an architecture to infer the appropriate sparsity pattern for the word embeddings while learning the sentence composition in a deep network. The proposed approach produces competitive results in sentiment and topic classification tasks with high degree of sparsity. It is computationally che...

Sentiment analysis on user-generated content has achieved notable progress by introducing user information to consider each individual’s preference and language usage. However, most existing approaches ignore the data sparsity problem, where of some users is limited model fails capture discriminative features users. To address this issue, we hypothesize that could be grouped together based thei...

Multi-target tracking is mainly challenged by the nonlinearity present in the measurement equation and the difficulty in fast and accurate data association. To overcome these challenges, the present paper introduces a grid-based model in which the state captures target signal strengths on a known spatial grid (TSSG). This model leads to linear state and measurement equations, which bypass data ...

Sparsity-based methods are widely used in machine learning, statistics, and signal processing. Thereis now a rich class of structured sparsity approaches that expand the modeling power of the sparsityparadigm and incorporate constraints such as group sparsity, graph sparsity, or hierarchical sparsity. Whilethese sparsity models offer improved sample complexity and better interpr...

In this paper, we study a Tikhonov-type regularization for restricted Boltzmann machines (RBM). We present two alternative formulations of the Tikhonov-type regularization which encourage an RBM to learn a smoother probability distribution. Both formulations turn out to be combinations of the widely used weight-decay and sparsity regularization. We empirically evaluate the effect of the propose...