Supplementary Documents for “Semi-Crowdsource Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning”
نویسندگان
چکیده
where T (A′) denotes the space spanned by the elements of the form uky and xv> k , for 1 ≤ k ≤ r, Ω(A′) denotes the space of matrices that have the same support toA′, ‖ ·‖ denotes the spectral norm and ‖ · ‖∞ denotes the largest entry in magnitude. Lemma 1. Let A∗ ∈ RN×N be a similarity matrix of rank r obeying the incoherence properties (A1) and (A2), with μ = max(μ0, μ1). Suppose we observe m1 entries of A∗ recorded in Ã
منابع مشابه
Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning
One of the main challenges in data clustering is to define an appropriate similarity measure between two objects. Crowdclustering addresses this challenge by defining the pairwise similarity based on the manual annotations obtained through crowdsourcing. Despite its encouraging results, a key limitation of crowdclustering is that it can only cluster objects when their manual annotations are ava...
متن کاملComposite Kernel Optimization in Semi-Supervised Metric
Machine-learning solutions to classification, clustering and matching problems critically depend on the adopted metric, which in the past was selected heuristically. In the last decade, it has been demonstrated that an appropriate metric can be learnt from data, resulting in superior performance as compared with traditional metrics. This has recently stimulated a considerable interest in the to...
متن کاملیادگیری نیمه نظارتی کرنل مرکب با استفاده از تکنیکهای یادگیری معیار فاصله
Distance metric has a key role in many machine learning and computer vision algorithms so that choosing an appropriate distance metric has a direct effect on the performance of such algorithms. Recently, distance metric learning using labeled data or other available supervisory information has become a very active research area in machine learning applications. Studies in this area have shown t...
متن کاملAn Effective Approach for Robust Metric Learning in the Presence of Label Noise
Many algorithms in machine learning, pattern recognition, and data mining are based on a similarity/distance measure. For example, the kNN classifier and clustering algorithms such as k-means require a similarity/distance function. Also, in Content-Based Information Retrieval (CBIR) systems, we need to rank the retrieved objects based on the similarity to the query. As generic measures such as ...
متن کاملHandwritten Text Line Segmentation by Clustering with Distance Metric Learning
Separating text lines in handwritten documents remains a challenge because the text lines are often ununiformly skewed and curved. In this paper, we propose a novel text line segmentation algorithm based on Minimal Spanning Tree (MST) clustering with distance metric learning. Given a distance metric, the connected components of document image are grouped into a tree structure. Text lines are ex...
متن کامل