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Claim this profileWorks330 from public data
- 4,891
- Generalizing from a Few Examples2,800
A thorough survey to fully understand Few-shot Learning and categorize FSL methods from three perspectives: data, which uses prior knowledge to augment the supervised experience; model, which uses prior knowledge to reduce the size of the hypothesis space; and algorithm, which uses prior knowledge to alter the search for the best hypothesis in the given hypothesis space.
- 936
- 780
- Transfer learning via dimensionality reduction727
A new dimensionality reduction method is proposed to find a latent space, which minimizes the distance between distributions of the data in different domains in a latentspace, which can be treated as a bridge of transferring knowledge from the source domain to the target domain.
- 504
- Combination of images with diverse focuses using the spatial frequency465
An approach that fuses images with diverse focuses by first decomposing the source images into blocks and then combining them by the use of spatial frequency is presented.
- Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network457
The proposed Temporal Hierarchical One-Class (THOC) network is a temporal one-class classification model for timeseries anomaly detection that captures temporal dynamics in multiple scales by using a dilated recurrent neural network with skip connections.
- Multi-Label Learning with Global and Local Label Correlation421
This paper proposes a new multi-label approach GLOCAL dealing with both the full-label and the missing-label cases, exploiting global and local label correlations simultaneously, through learning a latent label representation and optimizing label manifolds.
- Asynchronous Distributed ADMM for Consensus Optimization412
An asynchronous ADMM algorithm is proposed by using two conditions to control the asynchrony: partial barrier and bounded delay and achieves faster convergence than its synchronous counterpart in terms of the wall clock time.
- Improved Nyström low-rank approximation and error analysis355
An error analysis that directly relates the Nyström approximation quality with the encoding powers of the landmark points in summarizing the data is presented, and the resultant error bound suggests a simple and efficient sampling scheme, the k-means clustering algorithm, for NyStröm low-rank approximation.
- Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images347
Experimental results show that the proposed approach outperforms methods based on the intensity-hue-saturation transform, principal component analysis and discrete wavelet transform in preserving spectral and spatial information, especially in situations where the source images are not perfectly registered.
- Multifocus image fusion using artificial neural networks302
Experimental results show that the proposed method outperforms the discrete wavelet transform based approach, particularly when there is a movement in the objects or misregistration of the source images.
- Mining customer product ratings for personalized marketing272
This paper addresses some issues faced by recommender systems, and study how recent machine learning algorithms, namely the support vector machine and the latent class model, can be used to alleviate these problems.
- 237
- 228
- Simpler core vector machines with enclosing balls225
This paper introduces the enclosing ball (EB) problem where the ball's radius is fixed and thus does not have to be minimized, and develops efficient (1 + e)-approximation algorithms that are simple to implement and do not require any numerical solver.
- Texture classification using the support vector machines214
This paper applies SVMs for texture classification, using translation-invariant features generated from the discrete wavelet frame transform, and uses a fusion scheme based on multiple SVMs, each with a different setting of the kernel parameter.
- Efficient Multi-label Classification with Many Labels196
This paper addresses the problem of computationally inefficient multi-label classification by selecting a small subset of class labels that can approximately span the original label space by an efficient randomized sampling procedure.
- Accelerated Gradient Method for Multi-task Sparse Learning Problem194
An accelerated gradient method based on an ``optimal'' first order black-box method named after Nesterov and provide the convergence rate for smooth convex loss functions that significantly outperforms the most state-of-the-art methods in both convergence speed and learning accuracy.
- 193
- Learning with idealized kernels190
This paper considers the problem of adapting the kernel so that it becomes more similar to the so-called ideal kernel as a distance metric learning problem that searches for a suitable linear transform (feature weighting) in the kernel-induced feature space.
- 188
- Accelerated Gradient Methods for Stochastic Optimization and Online Learning186
The proposed algorithm, SAGE (Stochastic Accelerated GradiEnt), exhibits fast convergence rates on stochastic composite optimization with convex or strongly convex objectives and can be extended for online learning, resulting in a simple algorithm but with the best regret bounds currently known for these problems.
- Multi-Label Classification on Tree- and DAG-Structured Hierarchies185
The proposed algorithm is computationally efficient, easy to implement, does not suffer from the problem of insufficient/skewed training data in classifier training, and can be readily used on large hierarchies.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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