FurongHuang/TensorDecomposition4TopicModeling

spectrallda-tensorspark

Quick summary: This code implements a spectral (third order tensor decomposition) learning method for learning LDA topic model on Spark.

ConvDicLearnTensorFactor

Tensor methods have emerged as a powerful paradigm for consistent learning of many latent variable models such as topic models, independent component analysis and dictionary learning. Model parameters are estimated via CP decomposition of the observed higher order input moments. However, in many domains, additional invariances such as shift invariances exist, enforced via models such as convolutional dictionary learning. In this paper, we develop novel tensor decomposition algorithms for parameter estimation of convolutional models. Our algorithm is based on the popular alternating least squares method, but with efficient projections onto the space of stacked circulant matrices. Our method is embarrassingly parallel and consists of simple operations such as fast Fourier transforms and matrix multiplications. Our algorithm converges to the dictionary much faster and more accurately compared to the alternating minimization over filters and activation maps.

TTP-NeuralNets-Compression

We implement an efficient mechanism for compressing large networks by {\em tensorizing\/} network layers: i.e. mapping layers on to high-order matrices, for which we introduce new tensor decomposition methods.

OnlineTensorCommunity

TranscriptionFactorLatentTree

Structure and Parameter Estimation of Latent Tree Model on Yeast Transcription Factor