In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[EX Q]: Recommender systems play an increasingly important role in online applications to help users find what they need or prefer. Collaborative filtering algorithms that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix. The proposed method constructs a new representation which preserves affinity and structure information in the user-item rating matrix and then performs recommendation task. To capture proximity information about users and items, two graphs are constructed. Manifold learning idea is used to constrain the new representation to be smooth on these graphs, so as to enforce users and item proximities. Our model is formulated as a convex optimization problem, for which we need to solve the well known Sylvester equation only. We carry out extensive empirical evaluations on six benchmark datasets to show the effectiveness of this approach.
[EX A]: Top-N Recommendation on Graphs

[EX Q]: Clustering is a useful data exploratory method with its wide applicability in multiple fields. However, data clustering greatly relies on initialization of cluster centers that can result in large intra-cluster variance and dead centers, therefore leading to sub-optimal solutions. This paper proposes a novel variance based version of the conventional Moving K-Means (MKM) algorithm called Variance Based Moving K-Means (VMKM) that can partition data into optimal homogeneous clusters, irrespective of cluster initialization. The algorithm utilizes a novel distance metric and a unique data element selection criteria to transfer the selected elements between clusters to achieve low intra-cluster variance and subsequently avoid dead centers. Quantitative and qualitative comparison with various clustering techniques is performed on four datasets selected from image processing, bioinformatics, remote sensing and the stock market respectively. An extensive analysis highlights the superior performance of the proposed method over other techniques.
[EX A]: Variance Based Moving K-Means Algorithm

[EX Q]: Obtaining models that capture imaging markers relevant for<lb>disease progression and treatment monitoring is challenging. Models are<lb>typically based on large amounts of data with annotated examples of<lb>known markers aiming at automating detection. High annotation ef-<lb>fort and the limitation to a vocabulary of known markers limit the<lb>power of such approaches. Here, we perform unsupervised learning to<lb>identify anomalies in imaging data as candidates for markers. We pro-<lb>pose AnoGAN, a deep convolutional generative adversarial network to<lb>learn a manifold of normal anatomical variability, accompanying a novel<lb>anomaly scoring scheme based on the mapping from image space to a la-<lb>tent space. Applied to new data, the model labels anomalies, and scores<lb>image patches indicating their fit into the learned distribution. Results<lb>on optical coherence tomography images of the retina demonstrate that<lb>the approach correctly identifies anomalous images, such as images con-<lb>taining retinal fluid or hyperreflective foci.
[EX A]:
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery