Automatic feature learning algorithms are at the forefront of modern day machine learning research. We present a novel algorithm, supervised Q-walk, which applies Q-learning to generate random walks on graphs such that the walks prove to be useful for learning node features suitable for tackling with the node classification problem. We present another novel algorithm, k-hops neighborhood based confidence values learner, which learns confidence values of labels for unlabelled nodes in the network without first learning the node embedding. These confidence values aid in learning an apt reward function for Q-learning. We demonstrate the efficacy of supervised Q-walk approach over existing state-of-the-art random walk based node embedding learners in solving the single multi-label multi-class node classification problem using several real world datasets. Summarising, our approach represents a novel state-of-the-art technique to learn features, for nodes in networks, tailor-made for dealing with the node classification problem.
Factorization based techniques represent the edges in networks as matrices. These matrices are factorized to obtain the embeddings. The matrix representation and its factorization are done using various techniques @cite_0 @cite_20 @cite_17 @cite_18 @cite_6 . These methods may suffer from scalability issues for large graph datasets and sparse matrix representations need special attention.
@cite_18 @cite_6 @cite_0 @cite_20 @cite_17
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Matrix factorization techniques have been frequently utilized in pattern recognition and machine learning. Among them, Non-negative Matrix Factorization (NMF) has received considerable attention because it represents the naturally occurring data by parts of it. On the other hand, from the geometric perspective, the data is usually sampled from a low dimensional manifold embedded in a high dimensional ambient space. One hopes then to find a compact representation which uncovers the intrinsic geometric structure. In this paper, we propose a novel method, called Graph Regularized Non-negative Matrix Factorization with Sparse Coding (GRNMF_SC), the new model can learn much sparser representation and more discriminating power via imposing sparse constraint and Laplacian regularization explicitly. Experimental results on the ORL and Yale databases demonstrate encouraging performance of the proposed algorithm when compared with the state-of-the-art algorithms. Data often comes in the form of an array or matrix. Matrix factorization techniques attempt to recover missing or corrupted entries by assuming that the matrix can be written as the product of two low-rank matrices. In other words, matrix factorization approximates the entries of the matrix by a simple, fixed function---namely, the inner product---acting on the latent feature vectors for the corresponding row and column. Here we consider replacing the inner product by an arbitrary function that we learn from the data at the same time as we learn the latent feature vectors. In particular, we replace the inner product by a multi-layer feed-forward neural network, and learn by alternating between optimizing the network for fixed latent features, and optimizing the latent features for a fixed network. The resulting approach---which we call neural network matrix factorization or NNMF, for short---dominates standard low-rank techniques on a suite of benchmark but is dominated by some recent proposals that take advantage of the graph features. Given the vast range of architectures, activation functions, regularizers, and optimization techniques that could be used within the NNMF framework, it seems likely the true potential of the approach has yet to be reached. We propose a novel nonnegative matrix factorization model that aims at finding localized, part-based, representations of nonnegative multivariate data items. Unlike the classical nonnegative matrix factorization (NMF) technique, this new model, denoted "nonsmooth nonnegative matrix factorization" (nsNMF), corresponds to the optimization of an unambiguous cost function designed to explicitly represent sparseness, in the form of nonsmoothness, which is controlled by a single parameter. In general, this method produces a set of basis and encoding vectors that are not only capable of representing the original data, but they also extract highly focalized patterns, which generally lend themselves to improved interpretability. The properties of this new method are illustrated with several data sets. Comparisons to previously published methods show that the new nsNMF method has some advantages in keeping faithfulness to the data in the achieving a high degree of sparseness for both the estimated basis and the encoding vectors and in better interpretability of the factors. Matrix factorization based techniques, such as nonnegative matrix factorization and concept factorization, have attracted great attention in dimensionality reduction and data clustering. Previous studies show that both of them yield impressive results on image processing and document clustering. However, both of them are essentially unsupervised methods and cannot incorporate label information. In this paper, we propose a novel semisupervised matrix decomposition method for extracting the image concepts that are consistent with the known label information. With this constraint, we call the new approach constrained concept factorization. By requiring that the data points sharing the same label have the same coordinate in the new representation space, this approach has more discriminating power. The experimental results on several corpora show good performance of our novel algorithm in terms of clustering accuracy and mutual information.