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- Amit Gruber, Yair Weiss, Michal Rosen-Zvi
- AISTATS
- 2007

Algorithms such as Latent Dirichlet Allocation (LDA) have achieved significant progress in modeling word document relationships. These algorithms assume each word in the document was generated by a hidden topic and explicitly model the word distribution of each topic as well as the prior distribution over topics in the document. Given these parameters, the… (More)

- Amit Gruber, Yair Weiss
- Proceedings of the 2004 IEEE Computer Society…
- 2004

Multibody factorization algorithms give an elegant and simple solution to the problem of structure from motion even for scenes containing multiple independent motions. Despite this elegance, it is still quite difficult to apply these algorithms to arbitrary scenes. First, their performance deteriorates rapidly with increasing noise. Second, they cannot be… (More)

- Amit Gruber, Michal Rosen-Zvi, Yair Weiss
- UAI
- 2008

Latent topic models have been successfully applied as an unsupervised topic discovery technique in large document collections. With the proliferation of hypertext document collection such as the Internet, there has also been great interest in extending these approaches to hypertext [6, 9]. These approaches typically model links in an analogous fashion to… (More)

- Amit Gruber, Yair Weiss
- Computer Vision and Image Understanding
- 2006

We address the problem of segmenting an image sequence into rigidly moving 3D objects. An elegant solution to this problem is the multibody factorization approach in which the measurement matrix is factored into lower rank matrices. Despite progress in factorization algorithms, the performance is still far from satisfactory and in scenes with missing data… (More)

- Baback Moghaddam, Amit Gruber, Yair Weiss, Shai Avidan
- 2008 Information Theory and Applications Workshop
- 2008

We extend the l<inf>o</inf>-norm “subspectral” algorithms developed for sparse-LDA [5] and sparse-PCA [6] to more general quadratic costs such as MSE in linear (or kernel) regression. The resulting “Sparse Least Squares” (SLS) problem is also NP-hard, by way of its equivalence to a rank-1 sparse eigenvalue problem. Specifically,… (More)

- Amit Gruber, Yair Weiss
- NIPS
- 2003

The problem of “Structure From Motion” is a central problem in vision: given the 2D locations of certain points we wish to recover the camera motion and the 3D coordinates of the points. Under simplified camera models, the problem reduces to factorizing a measurement matrix into the product of two low rank matrices. Each element of the measurement matrix… (More)

- Amit Gruber, Yair Weiss
- SLSFS
- 2005

Matrix factorization is a fundamental building block in many computer vision and machine learning algorithms. In this work we focus on the problem of ”structure from motion” in which one wishes to recover the camera motion and the 3D coordinates of certain points given their 2D locations. This problem may be reduced to a low rank factorization problem. When… (More)

Latent topic models have been successfully applied as an unsupervised learning technique on various types of data such as text documents, images and biological data. In recent years, with the rapid growth of the Internet, these models have also been adapted to hypertext data. Explicitly modeling the generation of both words and links has been shown to… (More)

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