Latent Semantic Kernels

By Nello Cristianini, John Shawe-Taylor, Huma Mahmood Lodhi. Journal of Intelligence and Information Systems, 2001

321 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/942ed8a81716c1f1038ed091ca6149bc3ae22c8f

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Citations in the corpus

The Pyramid Match Kernel: Efficient Learning with Sets of Features (2007, methodology): “Figure 9 shows results for regressing directly on the year of publication for a NIPS paper using the classic bag-of-words approach (BOW), a standard approach applying LSI at the document level (LSI DOC) (Cristianini et al., 2002), and our method with bags of word meanings (BOWM PMK).”
Visual categorization with bags of keypoints (2002, methodology): “Our bag of keypoints approach can be motivated by an analogy to learning methods using the bag-of-words representation for text categorization [7]-[10].”
Structured Lexical Similarity via Convolution Kernels on Dependency Trees (2011, methodology): “The latent semantic kernels (Siolas and d’Alch Buc, 2000; Cristianini et al., 2001) derive G by applying LSA, resulting in a valid kernel.”
Canonical Correlation Analysis: An Overview with Application to Learning Methods (2004, methodology): “We explore the partial Gram-Schmidt orthogonalization (PGSO) algorithm, described in Cristianini et al. (2001), as our matrix decomposition approach.”
Learning Discriminative Projections for Text Similarity Measures (2011, background): “Representative approaches include latent semantic kernels (Cristianini et al., 2002), which learns an LSA-based ker-”
Predicting Tasks in Goal-Oriented Spoken Dialog Systems using Semantic Knowledge Bases (2013, background): “(Cristianini et al., 2002) (Wang and Domeni-coni, 2008) (Moschitti, 2009) found that open-242 domain semantic knowledge resources are useful for text classiﬁcation problems.”
Feature significance-based multibag-of-visual-words model for remote sensing image scene classification (2016, methodology): “The BOVW model originates from text analysis in which a document is represented by word frequencies without regard for their order.(34,35) These frequencies are then used to perform document classification.”
Detecting Management Fraud in Public Companies (2010, unknown): “There are many generic kernels in existence, and the list is ever growing (Cristianini et al. 2002, Joachims 1998, Rüping 2001).”
Deep Multimodal Representation Learning: A Survey (2019, methodology): “Alternatively, some approximation methods such as Nyström method [113], incomplete Cholesky decomposition [114], partial Gram Schmidt orthogonalization [115], and block incremental SVD [116] can be used to speed up this model.”
A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification (2014, methodology): “The traditional BOVW model was motivated by an analogy to learning methods using the bag-of-words (BOW) representation for text categorization (Joachims 1998; Cristianini, Shawe-Taylor, and Lodhi 2002) and has recently been successfully applied to image scene classification.”
A web-based kernel function for measuring the similarity of short text snippets (2006, background): “One of the early approaches in this vein is Latent Semantic Kernels (LSK) [5], which is a kernel-based extension to the well-known Latent Semantic Indexing (LSI) [6] proposed in the Information Retrieval community.”
Syntactic/Semantic Structures for Textual Entailment Recognition (2010, methodology): “Since the matrix P derived by applying LSA produces a semi-deﬁnite matrix (see (Cristianini and Holloway, 2001)) we can always use the similarity matrix derived by LSA in SSTK.”
Semantic Kernels for Text Classification Based on Topological Measures of Feature Similarity (2006, methodology): “Semantic Kernels The general concept of semantic smoothing kernels was for the first time introduced in [8] and subsequently revisited in [4, 6, 1], each time based on different design principles.”