We study the problem of multivariate regression where the data are naturally grouped, and a regression matrix is to be estimated for each group. We propose an approach in which a dictionary of low rank parameter matrices is estimated across groups, and a sparse linear combination of the dictionary elements is estimated to form a model within each group. We refer to the method as conditional spa...

Sparse coding is a method for nding a representation of data in which each of the components of the representation is only rarely signiicantly active. Such a representation is closely related to redundancy reduction and independent component analysis, and has some neurophysiological plausibility. In this paper, we show how sparse coding can be used for denoising. Using maximum likelihood estima...

Sparse coding theory demonstrates that the neurons in the primary visual cortex form a sparse representation of natural scenes in the viewpoint of statistics, but a typical scene contains many different patterns (corresponding to neurons in cortex) competing for neural representation because of the limited processing capacity of the visual system. We propose an attention-guided sparse coding mo...

Sparse coding is a method for finding a representation of data in which each of the components of the representation is only rarely significantly active. Such a representation is closely related to redundancy reduction and independent component analysis, and has some neurophysiological plausibility. In this paper, we show how sparse coding can be used for denoising. Using maximum likelihood est...

We investigate the application of sparse coding and dictionary learning to the discovery of sub-word units in speech. The ultimate goal is to generate pronunciation dictionaries that could be used for automatic speech recognition (ASR). A dictionary of sparse coding atoms is trained to code a subset of the TIMIT corpus. Some of the trained units exhibit strong correlation with specific referenc...

We study the problem of multivariate regression where the data are naturally grouped, and a regression matrix is to be estimated for each group. We propose an approach in which a dictionary of low rank parameter matrices is estimated across groups, and a sparse linear combination of the dictionary elements is estimated to form a model within each group. We refer to the method as conditional spa...

Although sparsity-based algorithm has emerged as an extremely powerful tool for information integration, it neglects the relationship of heterogeneous features and coding coefficients from the same class in the training stage, which may cause declining of the classification performance. In this paper, we focus on information integration for groundbased cloud classification in heterogeneous sens...

Computational models of visual cortex, and in particular those based on sparse coding, have enjoyed much recent attention. Despite this currency, the question of how sparse or how over-complete a sparse representation should be, has gone without principled answer. Here, we use Bayesian model-selection methods to address these questions for a sparse-coding model based on a Student-t prior. Havin...

Simultaneous sparse coding (SSC) has shown great potential in image denoising, because it exploits dependencies of patches in nature images. However, imposing joint sparsity might neglect the sight difference between patches. In this paper, we propose an image denoising algorithm based on robust simultaneous sparse coding (RSSC). In our algorithm, the sparse coefficient matrix is decomposed int...

Deep Convolutional Neural Networks (DCNN) require millions of labeled training examples for image classification and object detection tasks, which restrict these models to domains where such datasets are available. In this paper, we explore the use of unsupervised sparse coding applied to stereo-video data to help alleviate the need for large amounts of labeled data. We show that replacing a ty...