SLEP: sparse learning with efficient projections.Author(s): J. Liu, S Ji, J Ye, J Liu Publication date: 2009 Journal: Arizona State Univ. Read this article at ScienceOpen Bookmark There is no author summary for this article yet. Authors can add summaries to their articles on Science...
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SLEP: Sparse Learning with Efficient Projections Jun LiuShuiwang JiJieping Ye Jan 2011 437 被引用 · 0 笔记 收藏 Robust Visual Tracking via Structured Multi-Task Sparse Learning Tianzhu ZhangBernard GhanemSi LiuNarendra Ahuja Jan 2013 In this paper, we formulate object tracking in a particle fi...
33 The Bootstrap Learning Algorithm 20:49 A logarithmic improvement in the Bombieri-Vinogradov theorem 1:00:48 A Reintroduction to Proofs 1:00:18 Ratner_Masur equidistribution by orbit matching 52:22 Optimal transport in statistics and Pitman efficient multivariate distribution-f 44:27 Localized ...
III. 带有不同范式正则化的稀疏表示(SPARSE REPRESENTATION PROBLEM WITH DIFFERENT NORM REGULARIZATIONS) 分为五类: sparse representation with the l0-norm minimization [37], [38], sparse representation with the lp-norm (0 < p < 1) minimization [39]–[41], ...
Artificial intelligence Sparse Methods for Robust and Efficient Visual Recognition UNIVERSITY OF MARYLANDCOLLEGE PARK Rama Chellappa ShekharSumitVisual recognition has been a subject of extensive research in computer vision. A vast literature exists on feature extraction and learning methods for recognition....
Chandra, 2008, Efficient projections onto the 1-ball for learning in high dimensions: Proceedings of the 25th International Conference on Machine Learning, ACM, 272–279. [Gubernatis et al., 1977] Gubernatis, J., E. Domany, J. Krumhansl, and M. Huberman, 1977, The Born approximation ...
supervised dictionary learning (Mairal, et al., 2009) also train discriminatively, but effectively use an infinite-depth ISTA-like encoder, and are thus much less computationally efficient than DrSAEs. Supervised dictionary learning achieves performance statistically indistinguishable from DrSAEs using a ...
High-dimensional features, such as the convolutional neural network activations that drive many leading recognition frameworks, pose particular challenges for efficient retrieval. We present a novel method for learning compact binary codes in which the conventional dense projection matrix is replaced with ...
Learning an efficient projection to map high-dimensional data into a lower dimensional space is a rather challenging task in the community of pattern recognition and computer vision. Manifold learning is widely applied because it can disclose the intrinsic geometric structure of data. However, it only...