
	Where results make sense

Topic: Kernel PCA

	BioMed Central \| Full text \| Spectral embedding finds meaningful (relevant) structure in image and microarray data
		This result from kernel PCA is consistent with those obtained from the spectral methods of Ng et al., and demonstrates that each of these two nonlinear approaches have a dependence between the outcome of the classification algorithm and an appropriately optimized parameter.
		Kernel PCA was calculated with a Gaussian radial basis function kernel using the k.pca function in the 'kmethods' package of R [29].
		For example, in kernel PCA, a Gaussian radial basis function kernel is computed from the distance matrix and these entries are plotted against their respective Euclidean distances to represent the transformed space that the eigenfunctions are calculated on, in order to provide a low dimensional embedding.
	www.biomedcentral.com /1471-2105/7/74 (6074 words)

	On a Connection between Kernel PCA and Metric Multidimensional Scaling - Williams (ResearchIndex) (Site not responding. Last check: 2007-11-01)
		On a Connection between Kernel PCA and Metric Multidimensional Scaling - Williams (ResearchIndex)
		On a Connection between Kernel PCA and Metric Multidimensional Scaling (2001)
		On a connection between kernel PCA and metric multidimensional scaling.
	citeseer.ist.psu.edu /williams01connection.html (473 words)

	PCA Food & Beverage Encyclopedia
		Almond - The kernel of the fruit of the almond tree.
		Amaretto - A liqueur with the flavor of almonds (although it is often made from the kernels of apricot pits).
		Like bitter almonds, apricot kernels are poisonous until roasted.
	www.professionalchef.com /FrontoftheHouse/Encyclopedia/a.htm (2747 words)

	IEEE Xplore# Wrapper Result
		The kernel trick is used firstly to project the input data into an implicit space called feature space by nonlinear kernel mapping, then Fisher Linear Discriminant Analysis is adopted to this feature space, thus a nonlinear discriminant can be yielded in the input data.
		Another similar Kernel-based method is Kernel PCA, in which PCA is used in the feature space.
		The experiments are performed with the polynomial kernel, and this method is compared with Kernel PCA and FLDA.
	ieeexplore.ieee.org /search/wrapper.jsp?arnumber=1004157 (184 words)

	"De-noising and Recovering Images Based on Kernel PCA Theory" - Abstract (Site not responding. Last check: 2007-11-01)
		Principal component analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of the input data.
		Since Kernel PCA is nothing but a PCA in feature space, the projection of an image in input space can be constructed from its principal components in feature space.
		To make the above theories and applications persuasive, several experiments on both binary and gray images are delivered here, which include finding pre-images (exact or approximate) based on a small database composed of one Chinese character of different fonts, and another database of similar gray images of various gray-scale distributions.
	wscg.zcu.cz /wscg2004/Papers_2004_Poster/J07.htm (224 words)

	[No title] (Site not responding. Last check: 2007-11-01)
		本論文では，非線形特徴抽出手法の1つであるKernel PCA (KPCA)を追加学習可能なアルゴリズムに拡張し，非線形なオンライン特徴抽出手法としてIncremental KPCA (IKPCA)を提案する．IKPCAはKPCAによって得られる KPCA部分空間をデータの追加にともなって回転，および必要に応じて次元追加を行なうことで逐次的に更新する手法である．具体的には，1つデータが追加されるごとにまずそのデータの高次元特徴空間での一次独立性を判別する．次に寄与率を用いて新しい次元の追加判別を行ない，最後にKPCA部分空間を回転させることで更新を行なう．提案手法ではデータが入力空間において複雑な分布をもち，かつ逐次的にしか与えられない場合においても識別に有効な特徴量を抽出できる可能性が高い．またIKPCAは，高次元特徴空間において一次独立であるデータのみを保持しておくことで実行可能であるため，メモリコストの削減が可能である．これは追加学習の最大のメリットの一つである．
		Principal Component Analysis (PCA) is one of the feature extraction methods in pattern recognition.
		Incremental PCA (IPCA) is proposed as an online feature extraction method by introducing the incremental learning ability into PCA.
	www.kobe-u.ac.jp /ee3lab/summary/043T215.html (321 words)

	Citations: a connection between kernel PCA and metric multidimensional scaling - Williams (ResearchIndex) (Site not responding. Last check: 2007-11-01)
		Citations: a connection between kernel PCA and metric multidimensional scaling - Williams (ResearchIndex)
		The organisation of the remainder of this paper is as follows: In section 2 we introduce the technique of multidimensional scaling and relate this to the field of topographic mappings.
		A Kernel View Of The Dimensionality Reduction Of Manifolds - Ham, Lee, Mika, Schölkopf (2003)
	citeseer.ist.psu.edu /context/2045446/527292 (352 words)

	ingredient list
		Water, Ginseng Infusion, Apricot Kernel Oil, Chamomile Extract, Octyl Palmitate, Glyceryl Stearate, Ceteareth-5, Magnesium Ascorbyl Phosphate, Squalane (vegetable), Cetearyl Alcohol, Licorice Extract, Dimethicone, Tocopherol, Ascorbic Acid, Lemon Extract, Citric Acid, Cucumber Extract, Potassium Sorbate, Phenoxyethanol, Sodium Citrate, Bentonite Clay, Titanium Dioxide.
		Ginseng Extract, Kola Nut Extract, Apricot Kernel Extract, Cetyl Esters, Methyl Glucose Sequistearate, Calendula Extract, Squalane (vegetable), Tocopheryl Linoleate, Octyl Methoxycinnamate, Panthenol, Soya Sterol, Lactic Acid, Ginkgo Biloba Extract, Glycerin (vegetable), Arnica Extract, Hyaluronic Acid, Micellized Beta-Carotene, Echinacea Extract, Tocopherol, Licorice Root Extract, Beta-Carotene, Grapefruit Seed Extract, Allantoin, Potassium Sorbate, Ascorbic Acid.
		Aloe Vera Gel, Glycerin (vegetable), Panthenol, Sodium PCA, Potassium Alum, Hyaluronic Acid, Chamomile Extract, Comfrey Extract, Grapefruit Seed Extract, Sorbic Acid, Ascorbic Acid, Allantoin, PEG-10 Soya Sterol, Fragrance, Menthol, Annatto.
	www.earthessentials.com /ingredients.html (1214 words)

	Miguel Á. Carreira-Perpiñán: Papers
		Erdogmus, D., Carreira-Perpiñán, M. and Özertem, U. (2006): "Kernel density estimation, affinity-based clustering, and typical cuts".
		This contains a review of continuous latent variable models: probabilistic principal component analysis (PCA), factor analysis, the generative topographic mapping (GTM), independent component analysis (ICA), mixtures of latent variable models, etc. It also deals with issues such as parameter estimation, identifiability, interpretability, visualisation, and dimensionality reduction with continuous latent variable models.
		This contains a review of dimensionality reduction with nonprobabilistic methods (probabilistic methods, i.e., latent variable models, are reviewed in chapter 2): nonlinear autoassociators, kernel PCA, principal curves, vector quantisation, multidimensional scaling, Isomap, LLE, etc. It also reviews issues such as the curse of dimensionality and the intrinsic dimensionality.
	www.cse.ogi.edu /~miguel/papers.html (1171 words)

	EconPapers: Spectral Clustering and Kernel PCA are Learning Eigenfunctions
		EconPapers: Spectral Clustering and Kernel PCA are Learning Eigenfunctions
		Spectral Clustering and Kernel PCA are Learning Eigenfunctions
		Abstract: In this paper, we show a direct equivalence between spectral clustering and kernel PCA, and how both are special cases of a more general learning problem, that of learning the principal eigenfunctions of a kernel, when the functions are from a Hilbert space whose inner product is defined with respect to a density model.
	econpapers.repec.org /paper/circirwor/2003s-19.htm (341 words)

	CiteULike: scis0000001's kernel-pca (Site not responding. Last check: 2007-11-01)
		Recent papers added to scis0000001's library classified by the tag kernel-pca.
		A kernel view of the dimensionality reduction of manifolds
		posted to kernel-function kernel kernel-methods kernel-pca manifold by scis0000001 as
	www.citeulike.org /user/scis0000001/tag/kernel-pca (47 words)

	PCAUSA - Network Software Development Toolkits
		These samples demonstrate how to use the Windows transport data interface (TDI) API to access the Microsoft TCP/IP protocol from Windows NT and Windows 2000 kernel mode drivers.
		The Advanced TDI Samples also include a PassThru TDI Filter sample that can be extended to filter the TCP and UDP interface below Winsock.
		If you are a developer that wants to use TDI for TCP/IP networking from your kernel mode driver or need to perform filtering at the TDI level (below Winsock), consider starting with the Advanced TDI Samples from PCAUSA.
	www.pcausa.com (1022 words)

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