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47
The one-shot similarity kernel
- In International Conference on Computer Vision (ICCV
, 2009
"... face.com The One-Shot similarity measure has recently been introduced in the context of face recognition where it was used to produce state-of-the-art results. Given two vectors, their One-Shot similarity score reflects the likelihood of each vector belonging in the same class as the other vector an ..."
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Cited by 30 (7 self)
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face.com The One-Shot similarity measure has recently been introduced in the context of face recognition where it was used to produce state-of-the-art results. Given two vectors, their One-Shot similarity score reflects the likelihood of each vector belonging in the same class as the other vector and not in a class defined by a fixed set of “negative ” examples. The potential of this approach has thus far been largely unexplored. In this paper we analyze the One-Shot score and show that: (1) when using a version of LDA as the underlying classifier, this score is a Conditionally Positive Definite kernel and may be used within kernel-methods (e.g., SVM), (2) it can be efficiently computed, and (3) that it is effective as an underlying mechanism for image representation. We further demonstrate the effectiveness of the One-Shot similarity score in a number of applications including multiclass identification and descriptor generation. 1.
A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval
- IEEE TPAMI
, 2010
"... Abstract—Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metr ..."
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Abstract—Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, “similarity ” can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one goal without consideration of the other. This is problematic for medical image retrieval where the goal is to assist doctors in decision making. In these applications, given a query image, the goal is to retrieve similar images from a reference library whose semantic annotations could provide the medical professional with greater insight into the possible interpretations of the query image. If the system were to retrieve images that did not look like the query, then users would be less likely to trust the system; on the other hand, retrieving images that appear superficially similar to the query but are semantically unrelated is undesirable because that could lead users toward an incorrect diagnosis. Hence, learning a distance metric that preserves both visual resemblance and semantic similarity is important. We emphasize that, although our study is focused on medical image retrieval, the problem addressed in this work is critical to many image retrieval systems. We present a boosting framework for distance metric learning that aims to preserve both visual and semantic similarities. The boosting framework first learns a binary representation using side information, in the form of labeled pairs, and then computes the distance as a weighted Hamming
BoostCluster: Boosting Clustering by Pairwise Constraints
"... Data clustering is an important task in many disciplines. A large number of studies have attempted to improve clustering by using the side information that is often encoded as pairwise constraints. However, these studies focus on designing special clustering algorithms that can effectively exploit t ..."
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Cited by 17 (5 self)
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Data clustering is an important task in many disciplines. A large number of studies have attempted to improve clustering by using the side information that is often encoded as pairwise constraints. However, these studies focus on designing special clustering algorithms that can effectively exploit the pairwise constraints. We present a boosting framework for data clustering, termed as BoostCluster, that is able to iteratively improve the accuracy of any given clustering algorithm by exploiting the pairwise constraints. The key challenge in designing a boosting framework for data clustering is how to influence an arbitrary clustering algorithm with the side information since clustering algorithms by definition are unsupervised. The proposed framework addresses this problem by dynamically generating new data representations at each iteration that are, on the one hand, adapted to the clustering results at previous iterations by the given algorithm, and on the other hand consistent with the given side information. Our empirical study shows that the proposed boosting framework is effective in improving the performance of a number of popular clustering algorithms (Kmeans, partitional SingleLink, spectral clustering), and its performance is comparable to the state-of-the-art algorithms for data clustering with side information.
Predicting protein-peptide binding affinity by learning peptide-peptide distance functions
- In RECOMB
, 2005
"... Abstract. Many important cellular response mechanisms are activated when a peptide binds to an appropriate receptor. In the immune system, the recognition of pathogen peptides begins when they bind to cell membrane Major Histocompatibility Complexes (MHCs). MHC proteins then carry these peptides to ..."
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Cited by 10 (3 self)
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Abstract. Many important cellular response mechanisms are activated when a peptide binds to an appropriate receptor. In the immune system, the recognition of pathogen peptides begins when they bind to cell membrane Major Histocompatibility Complexes (MHCs). MHC proteins then carry these peptides to the cell surface in order to allow the activation of cytotoxic T-cells. The MHC binding cleft is highly polymorphic and therefore protein-peptide binding is highly specific. Developing computational methods for predicting protein-peptide binding is important for vaccine design and treatment of diseases like cancer. Previous learning approaches address the binding prediction problem using traditional margin based binary classifiers. In this paper we propose a novel approach for predicting binding affinity. Our approach is based on learning a peptide-peptide distance function. Moreover, we learn a single peptide-peptide distance function over an entire family of proteins (e.g MHC class I). This distance function can be used to compute the affinity of a novel peptide to any of the proteins in the given family. In order to learn these peptide-peptide distance functions, we formalize the problem as a semi-supervised learning problem with partial information in the form of equivalence constraints. Specifically we propose to use DistBoost [1, 2], which is a semi-supervised distance learning algorithm. We compare our method to various state-of-the-art binding prediction algorithms on MHC class I and MHC class II datasets. In almost all cases, our method outperforms all of its competitors. One of the major advantages of our novel approach is that it can also learn an affinity function over proteins for which only small amounts of labeled peptides exist. In these cases, DistBoost’s performance gain, when compared to other computational methods, is even more pronounced. 1
Semi-supervised learning via regularized boosting working on multiple semi-supervised assumptions
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2011
"... Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes all three semi-supervised assumptions, i.e., smo ..."
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Cited by 9 (0 self)
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Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes all three semi-supervised assumptions, i.e., smoothness, cluster, and manifold assumptions, together into account during boosting learning. In this paper, we propose a novel cost functional consisting of the margin cost on labeled data and the regularization penalty on unlabeled data based on three fundamental semi-supervised assumptions. Thus, minimizing our proposed cost functional with a greedy yet stagewise functional optimization procedure leads to a generic boosting framework for semi-supervised learning. Extensive experiments demonstrate that our algorithm yields favorite results for benchmark and real-world classification tasks in comparison to state-of-the-art semi-supervised learning algorithms, including newly developed boosting algorithms. Finally, we discuss relevant issues and relate our algorithm to the previous work.
Automatically Identifying Join Candidates in the Cairo Genizah
"... A join is a set of manuscript-fragments that are known to originate from the same original work. The Cairo Genizah is a collection containing approximately 250,000 fragments of mainly Jewish texts discovered in the late 19th century. The fragments are today spread out in libraries and private collec ..."
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Cited by 8 (7 self)
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A join is a set of manuscript-fragments that are known to originate from the same original work. The Cairo Genizah is a collection containing approximately 250,000 fragments of mainly Jewish texts discovered in the late 19th century. The fragments are today spread out in libraries and private collections worldwide, and there is an onging effort to document and catalogue all extant fragments. The task of finding joins is currently conducted manually by experts, and presumably only a small fraction of the existing joins have been discovered. In this work, we study the problem of automatically finding candidate joins, so as to streamline the task. The proposed method is based on a combination of local descriptors and learning techniques. To evaluate the performance of various join-finding methods, without relying on the availability of human experts, we construct a benchmark dataset that is modeled on the Labeled Faces in the Wild benchmark for face recognition. Using this benchmark, we evaluate several alternative image representations and learning techniques. Finally, a set of newly-discovered join-candidates have been identified using our method and validated by a human expert.
Automatic Paleographic Exploration of Genizah Manuscripts
"... The Cairo Genizah is a collection containing approximately 250,000 hand-written fragments of mainly Jewish texts discovered in the late 19th century. The fragments are today spread out in some 75 libraries and private collections worldwide, and there is an ongoing effort to document and catalogue al ..."
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Cited by 6 (4 self)
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The Cairo Genizah is a collection containing approximately 250,000 hand-written fragments of mainly Jewish texts discovered in the late 19th century. The fragments are today spread out in some 75 libraries and private collections worldwide, and there is an ongoing effort to document and catalogue all extant fragments. Paleographic information plays a key role in the study of the Genizah collection. Script style, and – more specifically – handwriting, can be used to identify fragments that might originate from the same original work. Such matched fragments, commonly referred to as “joins”, are currently identified manually by experts, and presumably only a small fraction of existing joins have been discovered to date. In this work, we show that automatic handwriting matching functions, obtained from non-specific features using a corpus of writing samples, can perform this task quite reliably. In addition, we explore the problem of grouping various Genizah document by script style, without being provided any prior information about the relevant styles. The results show that the automatically obtained grouping agrees, for the most part, with the paleographic taxonomy. In cases where the system fails, it is due to apparent similarities between related scripts. 1
Learning Bilinear Model for Matching Queries and Documents
"... The task of matching data from two heterogeneous domains naturally arises in various areas such as web search, collaborative filtering, and drug design. In web search, existing work has designed relevance models to match queries and documents by exploiting either user clicks or content of queries an ..."
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Cited by 4 (2 self)
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The task of matching data from two heterogeneous domains naturally arises in various areas such as web search, collaborative filtering, and drug design. In web search, existing work has designed relevance models to match queries and documents by exploiting either user clicks or content of queries and documents. To the best of our knowledge, however, there has been little work on principled approaches to leveraging both clicks and content to learn a matching model for search. In this paper, we propose a framework for learning to match heterogeneous objects. The framework learns two linear mappings for two objects respectively, and matches them via the dot product of their images after mapping. Moreover, when different regularizations are enforced, the framework renders a rich family of matching models. With orthonormal constraints on mapping functions, the framework subsumes Partial Least Squares (PLS) as a special case. Alternatively, with a ℓ1+ℓ2 regularization, we obtain a new model called Regularized Mapping to Latent Structures (RMLS). RMLS enjoys many advantages over PLS, including lower time complexity and easy parallelization. To further understand the matching framework, we conduct generalization analysis and apply the result to both PLS and RMLS. We apply the framework to web search and implement both PLS and RMLS using a click-through bipartite with metadata representing features of queries and documents. We test the efficacy and scalability of RMLS and PLS on large scale web search problems. The results show that both PLS and RMLS can significantly outperform baseline methods, while RMLS substantially speeds up the learning process.
