Efficient Multi-label Classification with Many Labels

By Wei Bi, James T. Kwok. 2013

194 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Elucidating image-to-set prediction: An analysis of models, losses and datasets (2019, background): “Multi-label classification has been a long lasting problem in computer vision [72, 21, 73, 67] tackled by a wide variety of methods, such as, decomposing the problem into independent single-label classification problems [44, 72], exploiting label co-occurrences [1, 56, 34], introducing priors such a”
Sparse Local Embeddings for Extreme Multi-label Classification (2015, methodology): “Techniques such as CS [3], CPLST [30], ML-CSSP [7], 1-vs-All [31] could only be trained on the small data sets given standard resources.”
Multi-Label Learning with Global and Local Label Correlation (2017, unknown): “These datasets have been widely used in multi-label learning literatures [7], [8], [11], [14], [15], [22], [26], [36], [37], [38], [39], [40], [41], [42] etc.”
Multi-label Classification via Feature-aware Implicit Label Space Encoding (2014, background): “As a special case of linear encoding, (Bi & Kwok, 2013) proposed an efficient randomized sampling procedure for selecting a column subset of the tagging matrix that can well span it, termed ML-CSSP.”
Design and Learning of Output Representations for Speech Recognition (2013, background): “317 There have been recent advances in output representation learning from the machine learning 318 community [1][30][17] based on latent variable modeling and large scale multi-label learning.”
Adversarial Multi-label Prediction for Spoken and Visual Signal Tagging (2019, background): “The first known challenge is the inability to deal with ‘so many’ labels [9, 13].”
Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications (2016, methodology): “As such, the Hamming loss [4, 6, 9, 10, 13, 18, 21, 39, 42, 47] continues to be one of the most popular losses for extreme multi-label learning along with precision [3, 5, 17, 20, 21, 33, 44, 45] and the F-measure [4, 10, 14, 18, 19, 21, 39, 49].”
Deep Extreme Multi-label Learning (2017, methodology): “mple passes down the well-established tree until it arrives at the leaf node whereby its labels are finally determined. The embedding based method is another fundamental technique to handle the issue [3, 6, 12, 17, 25, 35]. These approaches attempt to make the training and prediction procedures trac”
Deep Learning for Extreme Multi-label Text Classification (2017, background): “Many variants of the target embedding methods have been proposed [3, 6, 9, 10, 14, 19, 20, 24, 40, 46, 50].”
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications (2019, methodology): “Label embedding models (Balasubramanian and Lebanon, 2012; Chen and Lin, 2012; Cisse et al., 2013; Bi and Kwok, 2013; Ferng and Lin, 2011; Hsu et al., 2009; Ji et al., 2008; Kapoor et al., 2012; Lewis et al., 2004; Yu et al., 2014a) address the data sparsity issue with two steps: compression and…”
FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning (2014, methodology): “Methods mainly differ in the choice of compression and decompression techniques such as compressed sensing [19, 22], Bloom filters [12], SVD [29], landmark labels [4, 8], output codes [38], etc.”