4 research outputs found
Hierarchical Data Representation Model - Multi-layer NMF
In this paper, we propose a data representation model that demonstrates
hierarchical feature learning using nsNMF. We extend unit algorithm into
several layers. Experiments with document and image data successfully
discovered feature hierarchies. We also prove that proposed method results in
much better classification and reconstruction performance, especially for small
number of features. feature hierarchies
A deep matrix factorization method for learning attribute representations
Semi-Non-negative Matrix Factorization is a technique that learns a
low-dimensional representation of a dataset that lends itself to a clustering
interpretation. It is possible that the mapping between this new representation
and our original data matrix contains rather complex hierarchical information
with implicit lower-level hidden attributes, that classical one level
clustering methodologies can not interpret. In this work we propose a novel
model, Deep Semi-NMF, that is able to learn such hidden representations that
allow themselves to an interpretation of clustering according to different,
unknown attributes of a given dataset. We also present a semi-supervised
version of the algorithm, named Deep WSF, that allows the use of (partial)
prior information for each of the known attributes of a dataset, that allows
the model to be used on datasets with mixed attribute knowledge. Finally, we
show that our models are able to learn low-dimensional representations that are
better suited for clustering, but also classification, outperforming
Semi-Non-negative Matrix Factorization, but also other state-of-the-art
methodologies variants.Comment: Submitted to TPAMI (16-Mar-2015
Neural System Identification with Spike-triggered Non-negative Matrix Factorization
Neuronal circuits formed in the brain are complex with intricate connection
patterns. Such complexity is also observed in the retina as a relatively simple
neuronal circuit. A retinal ganglion cell receives excitatory inputs from
neurons in previous layers as driving forces to fire spikes. Analytical methods
are required that can decipher these components in a systematic manner.
Recently a method termed spike-triggered non-negative matrix factorization
(STNMF) has been proposed for this purpose. In this study, we extend the scope
of the STNMF method. By using the retinal ganglion cell as a model system, we
show that STNMF can detect various computational properties of upstream bipolar
cells, including spatial receptive field, temporal filter, and transfer
nonlinearity. In addition, we recover synaptic connection strengths from the
weight matrix of STNMF. Furthermore, we show that STNMF can separate spikes of
a ganglion cell into a few subsets of spikes where each subset is contributed
by one presynaptic bipolar cell. Taken together, these results corroborate that
STNMF is a useful method for deciphering the structure of neuronal circuits.Comment: updated versio
Deep matrix factorization for trust-aware recommendation in social networks
Recent years have witnessed remarkable information overload in online social networks, and social network based approaches for recommender systems have been widely studied. The trust information in social networks among users is an important factor for improving recommendation performance. Many successful recommendation tasks are treated as the matrix factorization problems. However, the prediction performance of matrix factorization based methods largely depends on the matrixes initialization of users and items. To address this challenge, we develop a novel trust-aware approach based on deep learning to alleviate the initialization dependence. First, we propose two deep matrix factorization (DMF) techniques, i.e., linear DMF and non-linear DMF to extract features from the user-item rating matrix for improving the initialization accuracy. The trust relationship is integrated into the DMF model according to the preference similarity and the derivations of users on items. Second, we exploit deep marginalized Denoising Autoencoder (Deep-MDAE) to extract the latent representation in the hidden layer from the trust relationship matrix to approximate the user factor matrix factorized from the user-item rating matrix. The community regularization is integrated in the joint optimization function to take neighbours' effects into consideration. The results of DMF are applied to initialize the updating variables of Deep-MDAE in order to further improve the recommendation performance. Finally, we validate that the proposed approach outperforms state-of-the-art baselines for recommendation, especially for the cold-start users. © 2013 IEEE